Staffing Engine

One score to rule them all, one score to find them, one score to bring them all, and in the data's clarity bind them. I found this article very interesting because it mentioned two different approaches used at Airbnb to boost data quality internally. https://medium.com/airbnb-engineering/data-quality-score-the-next-chapter-of-data-quality-at-airbnb-851dccda19c3?embedable=true Airbnb is big on the data scene, with lots of open source action, including Apache Airflow and Apache Superset, and a massive daily data flow. Yet, as their data game got more intricate, serving up precise and high-quality to a diverse crowd posed a bit of a challenge. data To tackle this, Airbnb initially rolled out a certification process (Midas) for critical datasets and metrics, aiming to rebuild trust among data consumers. Despite the intention, the certification strategy proved non-scalable, encountered resistance, and placed a disproportionate burden on the frontline data team. This observation is a mix of my past experiences with such processes and what they actually say in the article. As a result, there's been a move away from a simple "certified or not" system to a more teamwork-oriented model. In this refined approach, all teams, including both producers and consumers, play an active role in boosting data quality. Airbnb changed how it handles data quality, shifting from a strict to one focused on with the introduction of the Data Quality Score (DQ Score). This score serves as a solid measure of assessing the quality of data assets. Its purpose is to naturally drive data producers to collaborate with data consumers, motivating them to actively participate in enhancing the quality of the data they provide. enforcement strategy incentives While reading about the second approach, the DQ Score, described in the article, I was surprised to learn that it followed the first one, not the other way around. As you will see, the score is particularly polished, but bringing observability first to your stack is usually a good start. First, let's take a look at what Midas was - their existing data quality certification approach. I quickly skimmed through the document introducing the process. https://medium.com/airbnb-engineering/data-quality-at-airbnb-870d03080469?embedable=true In 2019, an internal customer survey revealed that Airbnb's data scientists were finding it increasingly difficult to navigate the growing warehouse and had trouble identifying which data sources met the high quality bar required for their work. So, the target here is really to help data consumption inside Airbnb and restore trust from data practitioners in Airbnb datasets. For a data quality guarantee to be relevant for many of the most important data use cases, we needed to guarantee quality for both data tables and the individual metrics derived from them. An important point they mention is raw datasets but also derived metrics. They encounter issues about consistency, timeliness,… Midas certification does not come without challenges. In particular, . Requirements for documentation, reviews, and input from a broad set of stakeholders mean building a data model to Midas standards is much slower than building uncertified data. Re-architecting data models at scale also requires substantial staffing from data and analytics engineering experts (we’re hiring!), and entails costs for teams to migrate to the new data sources. quality takes time There are four distinct reviews in the Midas process: Review the proposed design spec for the data model, before implementation begins. Spec Review: Review the pipeline’s data quality checks and validation report. Data Review: Review the code used to generate the data pipeline. Code Review: Review the source of truth metric definitions implemented in Minerva, Airbnb’s metrics service. Minerva Review: I don't doubt that the quality is there; I hope it is. However, it seems like a hefty cost to bear, particularly on the human aspect. Issues often arise in the implementation of certification or standardization processes. External reviews can disincentivize team members, hindering adoption and collaboration, and they may be perceived as a waste of time and an impediment. Collaboration and compromises are crucial. Baby steps and incremental approaches, rather than one-time sacred validations, prove effective. Inclusion is key, and genuine peer-to-peer communication, instead of top-down enforcement and hierarchy, helps to smooth the process. The worst part is that it also puts a lot of stress and responsibilities on the validation team. Because, at the end of the day, it's not a scalable approach. One team, as good as it is, cannot review and validate the contributions of all the other teams. As complaints grow and the backlog increases, the stress increases, and if more resources could help, the investments are generally not made against the majority complaining voice in an organization. We made the decision that we could to scale data quality at Airbnb, and we instead needed to of both the data producer and consumer. no longer rely on enforcement rely on incentivization I have been on the side of certification/standardization teams, and I know that adoption is key. To achieve that, you can only be a facilitator, an orchestrator of a process that has to be distributed, supported, and owned by everybody, and relinquish control. This second approach is a lot more sound! Now that we can feel the flow of this enforcement approach, let's look at how they decided to tackle the issues to incentivize the org to improve quality and boost adoption. With 1.4 billion cumulative guest arrivals as of year-end 2022, Airbnb's growth pushed us to an inflection point where diminishing data quality began to hinder our data practitioners. The sheer volume of transactions and data points logged per guest arrival is truly mind-boggling. Just for this guest booking, we're looking at a whopping 4 million per day. Now, picture the significant implications for the storage and computing infrastructure. 😍 And that's not all… guest arrival is only one of the many activities managed by Airbnb; you probably also have various datasets like listing details, user reviews, financial transactions, and operational metrics… all catering to different data practitioners. The goal is to maintain or enhance data quality even in the face of significant data growth. So, how did they improve the situation? In 2022, we began exploring ideas for scaling data quality beyond Midas certification. Data producers were requesting a lighter-weight process that could provide some of the quality guardrails of Midas, but with less rigor and time investment. … To fully enable this incentivization approach, we believed it would be directly tied to data assets. paramount to introduce the concept of a data quality score While everyone would agree that data quality is a prerequisite for any meaningful work, concerns may arise about the related costs, particularly if the established process tends to relieve individuals of their responsibilities. Tracking and improving this by itself is the . score incentive Guided by our principles, we eventually settled on having four dimensions of data quality: . Accuracy, Reliability (Timeliness), Stewardship, and Usability You're all familiar with the saying: 'If you can't measure it, you can't manage it,' attributed to Peter Drucker. In their case, it would be more fitting to state: 'If you can't it, you can't the need to address it, you can't the issues, you can't progress, you can't ensure , you can't set up effective , you can't drive , you can't stakeholders, and you can't . measure understand identify track observability alerting improvements convince foster adoption With the input of a cross-functional group of data practitioners, we aligned on these guiding principles: — score can be applied to any in-scope data warehouse data asset Full coverage — collection of inputs that determine the score is 100% automated Automated — score is easy to discover and actionable for both producers and consumers Actionable — score can be decomposed into pillars of data quality Multi-dimensional — scoring criteria and their definitions can change over time Evolvable And so, an important point for me is that this score, by being controlled by these five principles, stays updated once defined (or scripted, some kind of score as code), and it opens up a lot of different usages: monitoring, alerting, observability, explainability and more without an additional cost for the producers. Even better, they own it, and it becomes a living thing that they can fully visualize and nurture. This is why I was saying that it should be a prerequisite, a guiding light, even useful in the context of Midas. Let's wrap up this reading by delving into some details about this score. It provides a comprehensive understanding of the criteria to be considered as part of it and how to present it. Guided by our principles, we eventually settled on having four dimensions of data quality: . Accuracy, Reliability (Timeliness), Stewardship, and Usability We could also weigh each dimension according to our perception of its importance in determining quality. We considered 1) how many scoring components belonged to each dimension, 2) enabling quick mental math, and 3) which elements our practitioners care about most to allocate 100 total points across the dimensions: That's neat, but even if we understand that these individual scores are supposed to be computed automatically by some kind of pipeline, it allows too much room for interpretation by each individual team. This certainly leads to a lot of disparities between scores from different teams, rendering it useless for data consumers. However, by delving a little deeper, they provided enough guidance to achieve a good level of homogeneity and still avoid the pitfalls of the overarching previous certification approach. Much better. Now, we understand the great work done by the team of selected data practitioners to come up with these score definitions. It provides enough guidance to any team to create something that will be homogeneous across teams and still gives them enough flexibility and ownership to implement it. We can see that this is not a one-time effort but something that can evolve and is regularly and automatically updated. Our final design presents data quality in three ways, each with a different use case in mind: . We assigned categorical thresholds of “Poor”, “Okay”, “Good”, and “Great” .. Best for quick, high-level assessment of a dataset’s overall quality. A single, high-level score from 0–100 , where an asset can score perfectly on Accuracy but low on Reliability. Useful when a particular area of deficiency is not problematic (e.g., the consumer wants the data to be very accurate but is not worried about it landing quickly every day). Dimensional scores where data consumers can see exactly where an asset falls short and data producers can take action to improve an asset’s quality. Full score detail + Steps to improve, And then it provides enough detail for a data practitioner to have a bird's-eye view or delve deeper to fully trust the quality of the dataset. Note that Midas is still present for their more crucial data points: The DQ Score has not replaced certification (e.g., only Midas-certified data can achieve a DQ Score > 90). I'd question the necessity of keeping Midas. I get that abandoning it after investing so much in the process would likely be a tough decision. However, if they had opted to introduce the DQ score initially, would there have been a need for a subsequent certification process? I'm not entirely convinced, but then again, I wasn't there to witness the challenges of the pre-Midas era. Driving up quality through observation is a smart move. Navigating blindly without radar is definitely not a wise approach. That's why having a solid observation backbone in place is a very good start to guide and improve. And that's exactly what this score represents: your backbone, your radar. This score introduces a more nuanced grading approach, going beyond the binary certified/uncertified distinction and making it visible to everyone. At this point, we figure assigning this score becomes a local team thing, and it's supposed to be easier to pull off than getting a certification. Initially, their strategy involved compelling data teams to provide reliable and high-quality data to data practitioners. To achieve this objective, they implemented a certification process as a means of ensuring compliance and maintaining data integrity. However, with the second approach, they found a way to scale and, more importantly, federate everyone around the goal of while retaining ownership, accountability, and fostering adoption. This was achieved by understanding all the intricacies and maintaining the full liberty to build on top of it. improving data quality , Found this article useful? 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Data Quality Score: One Score to Rule Them All

There are two major ways to cooperate with a software or company: a dedicated team or a fixed-cost project. hardware development The suggests that your scope and project duration might change. Therefore, the total cost of the project will depend on the tasks you assign to the team, and you pay for the time engineers needed to accomplish these tasks. dedicated team model The means that you have all the requirements defined before starting the work, and you negotiate the total cost of, for instance, developing a product. fixed-cost project In this article we will explain when it’s more reasonable to go with a dedicated team model, how it might look, pros and cons, and how you can make this model more (and less) effective. Read also . Discovery Phase: One Small Step for You, One Giant Leap for Your Project Success How do you know if the dedicated team model will work for you? You might be a startup, an established business or a digital agency — it doesn’t make a difference; the dedicated team might work equally well for you. The first more important criterion is which you have. type of project A dedicated team is a right choice when you have a big project, several projects or a huge pool of tasks; when you don’t have a clear vision of future project deliverables; and when you know that many requirements will be revealed during the and . You know that the scope and timeline for this project might change, but the quality should stay on the same level. discovery phase R&D tasks The second benchmark for choosing a dedicated team is your . This type works when you have your own development team that you’re going to extend, you have an in-house project manager (PM) and you need an engineering team, you need a PM and a team, or you need an engineer with very specific expertise. staffing needs The third point is your . It’s very important not to be limited to a strict budget. budget There also might be a situation when you may consider switching . This makes sense when you have been working with a vendor for a certain period of time, so you know how the team works and estimates different tasks; the members have been shown to meet promised timelines; and you are satisfied with the quality level and trust your vendor. from a fixed-cost project to a dedicated team On the other side, this is not a one-size-fits-all model. The one of the following is true: you don’t trust your vendor, your budget is limited, or the project is small. It’s also not a good fit when your project plan is ready and very detailed, deadlines are defined, quality is a secondary priority for you, and you know that no changes to the scope might occur. dedicated team is not the right choice when How might your dedicated team look? Depending on how well you know the development process and what your staffing needs are, you might consider hiring only engineers or a team with a PM. When you hire only one or more engineers, you should clearly understand the development process, you are responsible for creating a task backlog, you will keep track of the work progress, and you should keep track of proper communication. When you hire a team with a PM, you might have significantly lower understanding of the development process, you will communicate only with the vendor’s PM, and the vendor’s PM will do all team management and keep track of the team lifecycle. What are the pros and cons of a dedicated team model? There will always be advantages and disadvantages no matter which cooperation model you choose. The dedicated team model offers you the highest flexibility for changes to planning and task priority, but, at the same time, you take responsibility for the communications and risks. The vendor also faces the pros and cons on its end, so you both always should negotiate to reach a win-win scenario. How to get the most and least effective collaboration So, when you’ve decided on a dedicated team, there are a few things that will help you be in this cooperation. more effective The first thing is your previous experience with dedicated teams or your understanding of the software development process. This will improve project management significantly. If your previous experience with software development was positive, this is a plus. The negative experience might cause a lack of trust in your vendor, leading to micromanagement and applying a process that helped fix issues last time, even if it’s not relevant. Second, especially when it’s a hardware project, you would benefit from having a highly skilled PM on your side, and you can predict the future impact of your engineering decisions (e.g., if you cut expenses now, you will end up with higher costs later). Other things impacting effectiveness are experience in interviewing tech staff, treating your dedicated team as your in-house team, visiting the dedicated team (preferably before starting the project), and motivating the team. Motivation influences performance a lot, and, with higher motivation, your team can do better than usual. So, here is what you can do to keep it at a high level: have a development process established, share a clear responsibility matrix, identify the goals, don’t use outdated technology stack, assign challenging engineering tasks, require very high-quality standards, swap staff responsibility from time to time to decrease instances of being “bored”. Read also . Guide on Managing Your Remote Product Development Team [Client’s Handbook] Some of your actions might also make your team , so we recommend avoiding the following tips. less effective Don’t work on tasks backlog. The team may often be unloaded and, after a certain time, become bored. Then, team members might ask to leave the project. Always rush deliveries. Teams will burn out quicker, especially if everybody understands that the rush has no purpose and always happens for no reason. Frequently introduce changes that completely cancel previous work. The team might treat new tasks as something that there is no reason to perform properly as they will be changed or removed anyway. Сommunicate with engineers unprofessionally, e.g. public conflicts, blaming, bullying. The team loses motivation, and any project-related activities are associated with something bad. Do not try to understand the development process. You should have a basic understanding of how the software is created; otherwise, the project management will become messy. Treat your dedicated and in-house teams differently. This might cause the customer’s in-house team not to feel accountable. Any issue might be recorded under the dedicated team. Summary The dedicated team is a really effective cooperation model and brings a lot of advantages, for example, you can select a team or apply any staff changes and be flexible with your project. However, prior to choosing this model, you’d better check whether the goals and needs of your project correspond. We recommend always aiming for a win-win situation with your vendor. This can be achieved when you have a professional PM, know the process, maintain a backlog, plan deliveries, and communicate properly and your vendor provides a strong team, proper expertise, and transparent reports. if you have some project idea in mind. Contact us

Is a Dedicated Team Model Right for You?

The following is a collection of hot topics across the tech ecosystem. With all the activity in AI and the rising popularity of ChatGPT, you may have wondered to what extent the software industry will be affected. Today, we're covering exactly that. 🍿 Quick Snack 🛠️ OpenAI's GPT-4 could impact 80% of US workers' jobs, but is the end for software engineers? 🍎 Apple's revenue from services alone is bigger than that of Nike and McDonald’s revenue combined, and the segment is twice as profitable as Apple's products division. 🤖 Despite layoffs in the tech industry, the SaaS market is still growing and companies are looking to implement robotic process automation to make staffing more productive and effective. 🍟 OpenAI CEO did not take equity, rival Character AI raises $150 million, and Intel's Chief Architect leaves to found a generative AI gaming software startup Extra Fries: 💸 +7 Funding Rounds 🍔 The Full Meal GPT-4's Anticipated Impact on the Workforce and the Future of Software Development OpenAI's GPT-4 could impact 80% of US workers' jobs, with 19% seeing at least half of their tasks affected, by researchers from OpenAI and the University of Pennsylvania. according to a paper Occupations with the highest exposure include mathematicians, tax preparers, writers, web designers, accountants, journalists, and legal secretaries. GPT's overall anticipated impact will be the strongest in the following industries: data processing services, information services, publishing industries, and insurance carriers. The industries that are anticipated to be the least affected are food manufacturing, wood product manufacturing, and support activities for agriculture and forestry. Of course, the study has its limitations, but the key takeaway is that GPT models will become more widespread. For example, on human developers. startups are already using GPT-4 to reduce costs The Future of Software Insights from The AI Maze The cost of producing software will decrease as AI improves, leading to a world of zero marginal cost software. Prediction: -Edmar Ferreira, Writer @ The AI Maze In software, utility is the most important factor. If two apps deliver the same utility, they are interchangeable. Software development will become more like writing, with a single developer being able to have a big impact. What does that mean? Convenience and differentiation will be the main forces driving value creation for companies building software. In the world of AI, convenience will be a function of price and complexity. The greater the complexity of the software, the less convenient it will be to create using AI. Convenience: For example, buying a subscription to Spotify is more convenient than downloading and organizing music on your own. Copycats will inevitably become more common. The more successful a software is, the more people will attempt to copy it. Companies will then have to rely on network effects (such as social networks and marketplaces), distribution, and switching costs (e.g. users becoming used to a particular workflow or ecosystem). Differentiation: Trends that will be at play: Consumers will benefit from highly customized software for their specific needs. The B2B SaaS market will be pressured to lower maximum prices as companies can make software at zero cost. AI-generated code will lead to end-user "programming" and internal tools teams in companies emerging as a powerful force in the software industry. Companies will need to work hard to deliver value above end-user custom solutions. Is It the End for Software Engineers? . Andrew Rousseau does not think so He argues that writing code is only a small part of a software engineer's job, and maintenance and defects constitute the largest portion of where time is spent. There is no debate that AI tools will unlock efficiencies in software engineering. However, software engineers will still need to be vigilant in checking the accuracy of generated code to ensure there are no defects. In other words, good software engineers will still be needed to ensure a balance between delivering features quickly and maintaining high-quality systems and software. Related News: Codium AI has released a beta version of its generative AI-powered code-integrity solution called TestGPT, which offers autogenerated software test suite suggestions for developers to speed coding and bug scans. Nvidia has announced new tools to aid developers and organizations working on advanced natural language processing (NLP), including speech AI and large language model (LLM) development. The Financial Dominance of Apple's Services Segment “Services” are now ~20% of Apple’s revenue. This segment encompasses the App Store, Cloud Services, Subscriptions, Apple Pay, Apple Care, and Advertising. To put it in perspective, Apple’s revenue from services alone is bigger than that of Nike and McDonald’s revenue combined. “Services”, as a whole, are also far more profitable to Apple. In 2022, Gross Margin% for services was twice that of products (~72% vs 36%). The Impact of Layoffs on SaaS Procurement and Renewals Despite the large number of layoffs in tech, the SaaS market is still growing steadily, with Gartner . predicting software spending to increase by 11.3% this year Vendr analyzed more than $2.5 billion in SaaS spending and . anticipates that SaaS spending will increase 18% this year Smaller, less mission-critical SaaS suppliers may face tougher negotiations and renewals, while prices for top sellers may increase due to inflation. Despite a bright outlook for some, the SaaS industry still needs to reduce friction in the procurement process and move towards standardized, transparent pricing to continue its growth. Will This Be the Rise of Automation? The COVID-19 pandemic has accelerated the demand for automation in businesses. Robotic process automation (RPA) is a software category that is experiencing strong demand due to its ability to make staffing more productive and effective. Even in the cost-conscious business climate, companies are still keen to implement RPA, generating tailwinds for RPA providers. We all know that in today's cost-conscious business climate, companies are looking for ways to cut costs and make investments more effective on a per-dollar basis. Reducing staff and making it more productive is one way to achieve this, and robotic process automation (RPA) may have an edge in this regard compared to other software categories. Battery Ventures' report on software spend shows that automation, including RPA and no code, "has risen to the upper-middle of the pack in Q1 from lowest-ranked category on average in Q3 2022 survey." The adoption of RPA is still in its early stages as companies are just beginning to explore the numerous use cases that exist across industries. Additionally, with AI tools rapidly improving, there is ample opportunity for startups to make an impact in this segment. 🍟 Extra Fries 👀 Sam Altman, the CEO, and co-founder of OpenAI, an equity stake in the company when it added the for-profit OpenAI LP entity in 2019. OpenAI is now looking to tender shares at a $29 billion valuation, more than double what it was worth in 2021. What?! did not take OpenAI rival Character AI has raised $150 million in a funding round led by Andreessen Horowitz, valuing the company at $1 billion. The company and role-play with anyone. ( 😂 I tried this already, and chatted with Elon Musk. offers AI chatbots that allow users to chat Raja Koduri, Intel's current Chief Architect, is leaving the company to . Koduri had assumed the role of leader of Intel's AXG graphics unit last year but stepped back to be a Chief Architect after a restructuring. During his tenure at Intel, Koduri was responsible for the creation and launch of Intel's Arc graphics cards and Ponte Vecchio GPUs. 🏃‍♂️ Expect more moves like this. found a generative AI gaming software startup is set to lay off 400 employees as part of its parent company Amazon's plan to cut 9,000 jobs across multiple divisions. The move is an effort to improve Twitch's business outlook in the long term. The comes after the company's CEO stepped down to spend time with his family. ✂️ The cuts continue. Twitch announcement 🖤 M&A Transactions Reveal, a leading AI-powered eDiscovery platform LIGL, a cloud-based digital evidence and lifecycle management platform. The acquisition will provide Reveal's clients with the most sophisticated workflow automation and digital evidence lifecycle capabilities available. has acquired 💸 Funding Rounds open-ended conversational applications that let users create characters and converse with them. Character AI | $150m: service management software for field service operatives. Joblogic | : $73m analytics and insights from unstructured image and video data. Coactive AI | Series A: $14m leading independent marketplace for flexibility services. Piclo | Series B: $9.8m operating system to start or grow airline retailing business. Mistifly | Series B: $8m AI-powered solution for radiotherapy treatment planning to treat cancer. MVision | Post-Seed: $5.8m real-time construction intelligence. Mercator AI | : $3.7m Also published here. 💬 If you have any feedback or suggestions for future topics, please don’t hesitate to reach out in the comments.

What OpenAI’s GPT-4 Means for the Future of Software Engineering

A full stack developer is a software engineer who has expertise in both front-end and back-end website development. They are skilled in front-end and back-end programming and can handle functioning on databases as well as UI/UX. The idea behind the term full-stack developers is that they are highly skilled in every element which makes a website works. If you're looking to recruit full-stack developers, then you can collaborate with the following top 10 organizations which specialize in offshore recruitment & IT staffing. 1. BorderlessMind BorderlessMind is a remote IT staffing firm that was incorporated in 1999 at Dallas, Texas. It offers a knowledgeable team that can handle practically any kind of website development, comprising full stack development, database resources, mobile applications, and UX/UI design for iOS, Android, and hybrid applications. 2. Toptal A specialized talent-matching platform called Toptal was initially developed with the needs of IT talent in mind. The company's developer part is its area of expertise, despite the fact that it has increased its talent pool incorporating illustrators and finance professionals. Employing a developer from Toptal is probably your best choice if you want to be assured that a full-stack developer is capable of handling the work. 3. Gigster A high-end recruiting network called Gigster connects technical talent with urgent assignments. Through Gigster, you can put together bigger teams, including a project manager representative who can help guide the project and serve as a point of contact between Gigster's team and your business. 4. Scalable Path With Scalable Path, projects are readily matched with freelance web developers. If you're hesitant, you might want to entrust Scalable Path or another talent matching provider with the job-screening process. Utilize this platform to hire full on-demand expert teams that also includes knowledgeable team leads as well. 5. Nexxt Employers may discover the ideal developers at the appropriate times and locations with Nexxt. To help businesses hire full-stack developers, Nexxt uses cutting-edge tech, a dynamic talent community, and multi-channel talent management solutions. 6. We Work Remotely A job portal specifically working for remote positions is We Work Remotely. Due to this emphasis, there are more tech and tech-creative hybrid job posts than usual, including front-end, back-end, and full stack developer opportunities. 7. Remote The main goal of remote, which was established in 2019, is to make it simple for businesses to hire people internationally. Their own crew is entirely remote and works from locations with different time zones. This portal provides a variety of job posting options, comprising of a free plan which offers you the most primary job options. 8. Hired Employers may search for IT engineers and full-stack developers, including those with expertise in front-end and back-end development, with the services offered by Hired. On Hired, you may find customized matches using their pipeline. Set up a company profile, use their search service to look for applicants, and instantly schedule interviews with those who meet your criteria. 9. People Per Hour People Per Hour features a sizable talented workforce, thousands of verified hours, and success stories from both freelancers and business owners. People Per Hour is an excellent option for employers who want to find and screen freelance applicants themselves because of how simple it is to post projects, get in touch with freelancers, and compensate for hours completed. 10. Outsourcely Outsourcely can be a fantastic platform to take into consideration if you're looking to employ a full stack developer in the long run. It provides independent contractors in a range of fields, including writing, video editing, website designing, customer care, software and full-stack development.

Where to Hire Global Full-stack Engineers in 2022

Series A Startup Guide to Outstaffing Software Engineers in 2022

Successful startups are built on strong, common ground: talented resources who help shape, build and transform a business idea into a reality. These resources can range from engineers, software developers, project managers, testers, business analysts, data scientists, and more, all of whom can be based anywhere in the world. After the challenges the IT industry, and every industry for that matter, faced in 2020, many organizations were prompted to adopt and embrace new ways of staffing their projects. With the entire world going remote, it began to make even greater sense to hire offshoring resources that could provide services with no ties to a physical location. Offshoring is no longer a trend, but a legitimate way of generating more business opportunities and value without breaking the bank. This is especially attractive for startup operations as budgets can be tight and it’s important to make every dollar count, especially in the early stages of your startup company. On top of costs, it’s also crucial to hire top-tier talent that can meet the modern business demands of the industry along with information technology trends. It’s safe to say that it’s virtually impossible to find all the talent your startup needs in a single location and at good value; talent is everywhere, so why should you limit your startup to geographic barriers? In this blog post, we’ll be unpacking how startup organizations can not only benefit from hiring offshore resources, but thrive by hiring the best available resources no matter where they’re located, giving a competitive edge in terms of support, costs, risk, and skills. Let’s dig right in! What is Offshore Software Development? Aspiring startups know that skilled software engineers are crucial to the success of their product or service, yet it can be really hard to find top-tier software engineers that are available or open to new opportunities. Outsourcing is the answer for companies looking to tap into the best tech talent budgets can afford, giving them an upper hand to streamline the software development lifecycle with resources that are already very knowledgeable on the subject and that have a variety of projects under their belt. Without embellishments, offshore software development consists of moving the software development process of your software projects to a country where production costs are lower. Now, for a little trip down memory lane, let’s take it back to the 1980s where Accenture, formerly known as Andersen, was popular for taking unstructured system requirements from clients and turning them into a working system for millions of dollars. While the term outsourcing wasn’t exactly used for this practice, Andersen was known for training consultants in software development similar to what today is known as the Waterfall methodology. Fast forward to the mid and late 1900s where India-based firms began to take projects offering programmers, system analysts, and project managers for a fraction of what US firms were charging. While they weren’t taking a bite of the large-sized projects yet, Indian firms began to set a precedent as highly-skilled, highly available resources. As the year 2000 was getting closer, and lots of companies were getting their systems ready for Y2K, offshore resources became the answer for many looking to get better costs. Twenty-one years later and things have evolved greatly. Now, project-based outsourced software development is more Agile than Waterfall, and India, while still one of the strongest countries to offer offshore software development solutions, is no longer the only country that offers these types of services as many others are beginning to earn a well-deserved reputation for reliable software outsourcing destinations. Eastern European countries like Ukraine, Poland, or Belarus are among the top outsourcing destinations. Latin American countries like Mexico, Brazil, and Chile also house large concentrations of software developers and are increasingly becoming a hot spot for outsourcing. In Asia, China and the Philippines are right behind India as the top countries that offer professional developers. Offshore vs In-house Software Development The decision to turn to offshore solutions for software development mainly stems from wanting to lower costs, as many companies are faced with constantly dealing with budgetary constraints. Building a software project from the ground up can be an expensive endeavor, so offshore resources are often the best path to follow. Choosing between investing in an in-house team of software developers or outsourcing software development to an offshore company is based on several criteria like budget, complexity of the project, and time to market, to name a few. In-house teams of engineers work in the employer’s facilities, making it a seamless workflow that offers a greater perceived control over project customizations, giving organizations direct control over picking and choosing the required skill levels that engineers must possess for a unique project. Offshore software development is a no-brainer if you’re looking to hire a wide range of expertise in software development tasks at a fraction of what you’d typically pay if hiring everything in-house. Offshore software development centers offer virtual services and remote employment so clients benefit from uninterrupted support and a streamlined process with their team extensions. Offshore software development allows companies to tap into many different areas of building a software product or service such as artificial intelligence, machine learning, data science, etc., all of which can be hard and expensive to find in a single geographic location. If your organization chooses to go with the in-house team direction, you need to be prepared to pay for a premium as in-house teams are more expensive to build and maintain. Oftentimes, companies look at in-house teams as a means to have similar language and cultural background, thus speeding up software development but with how interconnected the world is nowadays, this can be virtually achieved anywhere in the world. While offshore software development centers are typically in non-English speaking countries, professionals almost always speak the language fluently to communicate effectively with clients. Here’s a comparison of offshore and in-house software development across several areas: . It’s a lot more expensive to recruit, train, hire, onboard, and grow an in-house team of developers than offshore software development resources. Costs . Hiring in-house engineers can prove challenging when you’re talking about different skill sets needed for a project. For instance, Data Scientists or an engineer specialized in a specific programming language are not that easy to come by and it may be the case that you don’t find a suitable candidate in your region, which is not a problem in offshore centers where they can easily access their pool of resources across the globe and hand clients the right professional for the specified task. Breadth of specialized skills . Many believe that in-house teams possess inherently better communication because they have direct access to other owners of the software development process. While this may be the case, it’s also true that the pandemic truly broke down a lot of barriers in the regard that nearly everyone went remote, so virtual communication became the norm which is something that offshore software developers were already experts at, making it easier to communicate with clients, teams, stakeholders, etc. Communication . While in-house teams seem to have the upper hand in this area, it’s also worth mentioning that offshore developers give clients the ability to offer support around the clock. Also, as stated earlier, the current health crisis pushed in-house employees to go remote as well, putting everyone in the same square when it comes to communication. Timezone and language Advantages of Offshore Development for Startups Every startup is different, but they’re all in search of the best talent. When weighing the pros and cons of accessing the talent pool of offshore software development, startups have a lot to consider. This process can be easier by doing research, reviewing portfolios, or reading case studies to see what best practices apply to your unique situation. After looking at the hard facts, it won’t be difficult to see the many benefits you’ll gain by engaging with a software development company, mainly in the budget department. Here are some of the top advantages of working with an offshore software development company for your startup: . Startup companies are typically looking for every way possible to lower costs and hiring an in-house team of software developers can be a luxury that you simply can’t afford at this stage of your company. By hiring an offshore software development company, not only do you significantly lower the costs of developing your product or service, you also access a pool of resources that would otherwise be virtually impossible to achieve if you were to build an in-house team of developers. Money . Offshore software developers work with lots of projects that give them exposure to new technologies, methodologies, ways of doing business. All of this adds to their skill set, making it easier to perform better and improve your project’s efficiency. When choosing a competent vendor, you can get a holistic software development approach to your project that includes consulting, analytics, testing, UI/UX design, project management, and any other service your startup will inevitably need. Performance . Startups thrive on flexibility, so it’s important to have a team of developers that easily adapt and respond to changing business and market demands. If you were to have an in-house team, it’s traditionally more time-consuming to ensure everyone aligns to a specific goal or find the right candidates that can meet the needs of a specific technology. By putting the software development tasks in the skilled hands of the right offshore team can significantly free up your time so you focus on other vital areas of your startup like Marketing, Financials, searching for investors, go-to-market strategies, and more. What’s more, offshore software development teams offer reliable and dedicated professionals who have proven expertise and knowledge on unique technical areas that can make your product or service more effective, all within an agreed-upon deadline. Flexibility Checklist for Hiring an Offshore Development Team The Covid-19 pandemic is forcing entire industries to reshape the way they do business. Offshoring continues to cement its place as the go-to solution for startup companies looking to hire the best talent across the globe, managing a balancing act between costs, skills, and experience; a feat that can boost the success of your startup to greater heights. Okay, so you’ve decided that what’s best for your company is to go with an offshore development team...but how do you choose the best one? Worry not! We’ve prepared a handy checklist that you can use as the basis of your selection process to land a quality vendor who will take your project and needs, and make them a priority the way you need them to. . By delineating specific parameters, it’ll be a lot easier to find vendors that have professionals who fit the bill. Build a robust, specific job description of the type of professionals that you need for your software development project . Perform background checks for legitimacy . See how many clients they’ve worked with, the size of the clients they’ve worked with, and the type of culture they promote. Are employees happy? Employee satisfaction says a lot about the success and wellbeing of a software development vendor. Look at the vendor’s track record and industry reputation . Ask about similar projects and how long it took them to deploy successfully . By looking at other clients’ real data, you’ll get a clearer picture of the type of vendor that can suit your startup’s needs. Read case studies of similar startups to your own to see the vendor they used and why it worked for them . Consider that specialized roles will incur slightly higher salaries, so be mindful of the project requirements so the vendor can assign the right roles to your startup. Compare prices between at least 2-3 vendors to get an idea of the average pay Ask about the vendor’s style of work, the methodologies they most often employ, and some of the latest technologies they’ve worked with. . Confirm the typical cadence to show MVPs, prototypes, or demos so you get an idea of what to expect for your project . As offshore teams are an extension of your team, confirm with vendors that you’ll hold total control over development . Confirm the type of communication channels they use to guide a project to completion Freelance Platform or Outsourcing Software Development Company? As of late, hiring software developers under a freelance model is gaining popularity. Without the legal ties of a contract to a major outsourcing software developer, startups are tempted to go with the freelancing route but it can easily backfire if not done properly as it’s more complex to confirm the legitimacy and reputation of a freelancer. While it’s true that software developers of offshore companies can cost a little bit more than freelancers, it’s also true that freelancers can’t guarantee their work in your project or maintenance after project completion. In short, outsourcing software developers give you a significant and tangible upper hand in terms of security. Freelancers, thanks to their self-employed nature, manage their time based on the number of projects they perceive they can undertake at a given point in time. When choosing to outsource software developers, the vendor has a management team that carefully assigns resources to a project based on their skills, expertise, and availability, so as not to overload them and give the client a satisfying end result. What’s also worth mentioning is that in an outsourcing model, it’s easier for developers to access numerous resources and technologies, making it easier and more effective to guarantee high-performing software products or services, which is not the case with freelancers. Freelancers rely on their own equipment to get the job done and are best suited for small, specialized tasks rather than a full-fledged project like a startup. First published here

Hiring Offshore Engineers for Startup Founders

Becoming a manager is usually one of the biggest challenges of an engineer’s career. We are usually used to algorithms and state machines, which are predictable and have specific outputs depending on the input. But managers (engineering managers, in particular) need to deal with people. And people are not predictable. It isn’t a matter of hard skill. Managers need to invest in soft skills to indeed be useful. The manager’s skills pack has two different tasks: to manage and to lead. So it’s crucial to understand what each term means. What’s the difference between Management and Leadership? Every manager should be a great leader, but a leader doesn’t necessarily is a manager. According to John Kotter, Harvard Business Review professor, management is about responding to complexity. The main job of a manager is to deliver outcomes in an orderly (predictable) way. Planning, budgeting, and staffing are examples of management. It means using spreadsheets, organizing documents, making hard decisions about processes and people. Leadership, on the other hand, is about producing and responding to change. As professor Kotter states, leadership activities include setting direction, aligning people, and providing motivation. It involves pushing the team forward to meet the managing directions. There are plenty of leadership materials out there. So, in this article, I want to share some thoughts on the challenges every engineer manager goes through to manage a team. I want to talk about hard skills for having visibility and predictability over the team’s work. On having visibility over the process Imagine how pilots control the aircraft. They need to fly up to a given distance from the ground, to follow a given route, to reach a given velocity, to keep the cabin pressurized, and so on. On the outside, whether, pressure, gravity, and other elements impact on the flight, and pilots need to respond accordingly to keep it in the air. Metaphorically, engineering managers have the same job. External factors like culture, engineer’s personal life and aspirations, business, and company’s goals affect the development flow. That’s why I think the most relevant job for managers (in the point of view of management) is to create a proper cockpit. It must communicate the metrics of process and provide data to point variations and state of every single step of the process. Just like a pilot needs to correct the aircraft’s altitude because there’s a storm ahead, a manager needs to correct WIP when developers open too many pull requests and stop closing them. That’s the dynamic. You see something not going well, and you act; then, you keep watching the metric until the route is corrected. If you don’t know what to measure, you can start by reading: https://hackernoon.com/5-metrics-engineering-managers-can-extract-from-pull-requests-h41332yl https://hackernoon.com/50-shades-of-lead-time-measuring-each-part-of-the-development-process-zt6333yv It’s fundamental to control flow metrics. By keeping them stabilized, you can achieve predictability much easier. On having predictability over the process Predictability has been in the center of software development debates for years. There are lots of techniques and strategies. For forecasting deliveries, product managers have been simply summing up Story Points or Point of Functions, for instance. It usually doesn’t work. You can see it by yourself. Plot a histogram of many times a User Story graded with 8 User Points takes from Backlog to Deploy. Probably, the variation is among user stories is enormous, although the grade attributed is the same. The work to move a card throughout the development flow is unpredictable. And it’s engineering managers’ responsibility to tame this unpredictability by controlling the system. Predictability comes from paced and cadenced work. Some managers need to define some numbers in advance to define OKRs, or to commit with some improvements. In this case, I prefer using statistics rather than a simple average. While I worked at Plataformatec, we did several experiments with the . It may sound not very easy, but actually, it is quite simple. As Lucas Colucci simplified: . Monte Carlo method it’s like brute force, but for forecasting If you’re not interested in getting too serious about statistics, you may calculate the median or percentiles of delivered work. Do yourself a favor, don’t go for the average. It hides too much information. If your median throughput is 15 work items delivered per week, then you can expect next week’s iteration to have a similar number. If 75% of last weeks’ work items delivered were 20 or less, then next week’s throughput should be near 20 with 75% of confidence. If you need to set a date, do the same math, but use Lead Time instead of Throughput. If 95% of the last month’s work took 15 days or less to be done, then multiply the number of expected items to be delivered by 15 days. Conclusion Becoming a manager implies being good at management and leadership. In this article, I talked about two challenges of the management: control and predictability. I shared some advice on how to control the development flow, and how you can achieve predictability from it. The article does not cover everything on the matter of management. But it can help just-promoted-engineers to understand what is expected from them from now one. For more articles and tips, ! subscribe to our engineering management newsletter Previously published at https://sourcelevel.io/blog/becoming-a-manager-of-engineers-focusing-on-visibility-and-predictability

Becoming a Manager of Engineers: How to Focus on Visibility and Predictability

( Photo by Alvaro Reyes on Unsplash ) How do you know what the value of a product or service is? How do you know what the value of a product or service is to someone else? How do you know what they will pay for that value? How do you know what you can, or need to, charge for that value? These are the core questions of pricing. Pricing is its own specialized field of research in economics, psychology, and business. There are research firms and consulting shops built entirely around helping businesses find the best possible price for your thing for your market. In this post, we’ll talk through how to think about pricing from a marketing perspective. Programming notes: this post is n in a series of indeterminate length on GTM topics mainly for startup people, mainly leadership, mainly coming from non-GTM backgrounds. There’s a list at the end. Pricing is marketing Pricing broadcasts explicit and implicit messages about your product. Much of which you only have tenuous control over. For similar products, is this a cheaper alternative, on par with the average, or a premium? Relative to similar products, do the capabilities and value created align with the price? Is it cheap because it does less or is it a better value because it does the same for less? Is it premium because it does more, or something special, or something niche? Is the pricing and how pricing scales transparent and predictable? Or opaque and unpredictable? Are you easy to do business with or hard to do business with? Does the price structure and unit of consumption match the target buyer’s procurement process and how they’re used to paying for things? Is this price marketable inside the org? Will paying this price make me look good or bad to my boss? Corollary: the pricing page is a marketing page and pricing page actions are significant indicators of qualification and intent. Advice! Pricing needs a marketing intent and purpose. Know where you want to fall on questions like those above and explicitly find a price structure that gets you in the vicinity. Then validate that that’s the case with your actual market. Then decide how to communicate that online, if at all, and make sure that it’s perceived how you expect it to be by your actual market. More advice! Are you a startup? You’re probably just going to look at what competitors charge and what it it is you think you need to charge to bring in the right amount of revenue given some assumptions about how much of the market you can capture in order to hit some more or less arbitrary milestone set by an investor in order to raise the next round of funding. You should work out everything discussed above after the fact. This will form a useful baseline to inform pricing and other go to market decisions in the future. still Pricing has positioning power Pricing is a signal of what category a customer should put your product in. Not just in terms of being on the scale of fungible commodity to bespoke premium service. But also in the sense of what you are comparable to. Example! If you sell a marketing automation product but meter and charge on a purely usage based model — as opposed to database size x feature package — are you really selling a marketing automation product? Another example! If you sell a premium service that customizes and installs made-to-measure software using a heavy forward deployed engineer model but charge less (net) than a pre-packaged COSS product, are you really selling an unmaintainable erector set? Pricing changes sales cycles The more you charge, the higher the power needed to say yes to paying for it. The higher the power needed to say yes, the more work your sales team has to do and the more likely you are to need a sales team in the first place and the more people you’ll have to convince of the value you provide and the more value you’ll have to provide and/or the more critical your product will need to be (need to have not want to have) and the stronger the business case for it will need to be and the more negotiation that will take place and the more likely it is that there will be someone on the customer side whose sole purpose is to negotiate your price down and the longer your sales cycle will be. Etc. On the flip side, the less you charge, the more likely it is that if your product is even halfway good that it will spread like wildfire because everyone has the authority to pay nothing for something.* *Except for specifically regulated industries where even free things take multiple layers of approval. Pricing is friction. Growth vs Revenue Another way to think about sales cycles is in a frame of growth vs revenue. The less you charge, the more likely it is you will grow. The more you charge, the more money you will make. This would seem non-controversial on the face of it. But it’s not remotely how the real world works. Charging nothing might make it seem like your product has no value. Charging any specific price might limit your adoption so much that you never get enough revenue or adoption or growth to either raise money or have a sustainable business. One of the reasons VCs care about product-market fit is that it indicates there is path by which you might figure out this tradeoff in a way that generates money either through investment or revenue or some combination. some Pricing vs Packaging Packaging is how you organize what you sell for consumption. Are there different tiers? Do customers get different versions of the product depending on how much they pay?Do customers get differentiated service depending on how much they pay? Is there a base fee for access to the platform and then additional fees for different capabilities or additional scaling units? What is the unit of consumption? What is the unit of price? Does the pricing unit scale with the cost unit (fixed margin!) or superlinearly (growing margin!) or sublinearly (negative margin at scale!)? Is everything a la carte?Etc The combination of pricing and packaging is a strategic decision that determines margins and how revenue scales for your business. Whether you make it strategically or not is another question These decisions, once operationalized by staff and built into your systems, are to change. hard When you are early stage, you inevitably change them and end up with some customers on one pricing + packaging scheme and others on a different one. Some of those customers will be aggrieved and you’ll have to manage that. Advice! Try not to mess with this more often than twice a year when you’re tiny and once a year when you’re any bigger than tiny. The longer you wait to automate this and embedding it into organizational practice, the more degrees of freedom and optionality you will have to find an optimal-for-now price structure. This is not for the feint of heart. But if you can stomach it, do it. A small optimization here will pay compounding dividends into an unpredictable future. Pricing is an elemental part of going to market Your pricing should complement your messaging, your design, the nature of the problem you solve, the power seniority of your customers, the budget authority of your customers, the money you help people save, the money you help people make, the pain you take away, the sales experience your prospects have, the ease or difficulty of implementing your solution, etc. Your pricing should not cause cognitive dissonance. More advice! I have extensive experience trying to change how buyers think about the unit of consumption of some product or service. Even when we were providing greater value at a lower price — because the model was different from how they were used to thinking about, modeling the future cost of, and procuring — we were inevitably pushed into either reworking our pricing to match customer expectations or (maybe worse!) creating an overlay pricing model with a bunch of fine print where we still priced the same way as we always had but changed the of it so it was easier for customers to deal with. The last is a landmine that someone inevitably steps on when their expectations deviate from reality. One of the parties will eat that deviation in lost dollars. presentation Pricing approaches This is a rundown. Please consult an expert if your needs are more sophisticated (color me skeptical). Or, as says: just charge more. very basic Patrick McKenzie Cost plus (least thought) Basic: charge some uplift on your cost of providing the product or service Less basic: charge some uplift on the of providing the product or service, including staffing, real estate, etc etc fully burdened cost Value (most rev per account) Basic: charge some amount that’s reflective of the perceived, market set, or realized value you provide to the customer. This amount might be completely arbitrary. Less basic: have a business case that proves the independent range of value a customer will realize, like money saved or money made or PR disasters averted etc Growth (land grab) Basic: charge zero Less basic: charge just enough so that people feel committed to giving the product a shot but not so much that there’s any real friction to purchasing Intermediate: freemium Land and expand (more revenue tomorrow than today per account) Basic: charge little enough that it’s super easy to adopt with the intention of monetizing expanded usage through a growing license/subscription base within an account over time — this is effectively what freemium is Less basic: the SFDC model where you land at the individual and then sell to the manager of individuals and then sell to the manager of managers, working your way up an org over time until you’re indispensible Advice! You can mix and match the above! People do it all the time. You can spend hours, days, weeks, and months figuring out what to do. Don’t do that. Get in market and get feedback. Know what kind of business you’re trying to create and manage your risks for that kind of business. More advice! However you charge, ultimately you want price to scale linearly with cost — preferably super-linearly, which really means that cost scales sub-linearly as you provide the product/service because you get economies of scale on one or more dimensions. at least Which means margins are consistent or growing. Even more advice! On the flip side, if pricing scales sub linearly with cost, you need some confidence that 1) you can capture enough of the market or have rocket ship growth such that you have so much revenue that it doesn’t matter or 2) that you will find additional sources of revenue in the future through up-sell, cross-sell, data sell, selling your users/customers to third parties, etc. Posts in this series (and templates) Marketing 101 for Engineers: A Functional Introduction Marketing 102 for Engineers: Roughing Out a Funnel Marketing 201 for Engineers: Messaging & Positioning Marketing 202 for Engineers: Launching Marketing 203 for Engineers: Sales Enablement Marketing 204 for Engineers: Generating Demand Marketing 301 for Engineers: Strategy & Planning Marketing 302 for Engineers: Hiring Marketers Marketing 303 for Engineers: Pricing Frame Sales 101 for Engineers: A Functional Introduction PR 101 for Engineers Analyst Relations 101 for Engineers Basic Messaging Template [Google Doc] Basic Funnel Metrics Template [Google Sheet] Basic Launch Timeline Template [Google Doc] Basic Battlecard Template [Google Doc] Detailed Battlecard Template [Google Doc] Basic Marketing Calendar Template [Google Sheet] Basic Marketing Ladders Template [Google Sheet] Reading List and Resources Natalie Louie from Hired on pricing SaaS products Ibbaka 4 part series on pricing evolution over the life of a product and market adoption Price Intelligently

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