Three Manitobans have been named to the prestigious WXN (Women’s Executive Network) 2023 Canada’s Most Powerful Women: Top 100 Award Winners — a senior executive at Canada Life, a chemical research scientist at the University of Manitoba and a senior vice-president at SkipTheDishes.

Three Manitobans named to Canada’s Most Powerful Women list

As voted by our readers in the 2023 Best of MTL readers poll, the best food delivery platforms in Montreal are by Uber Eats, DoorDash, SkipTheDishes, Radish and Amazon.

Best Food Delivery Platforms in Montreal: Best of MTL 2023

SkipTheDishes, Canada's only homegrown food delivery network, is gearing up for another exciting NHL® season, delivering meals, groceries and more from coast to coast. As the official food delivery app of the NHL® since 2021,

Skip Launches Latest Evolution of Wearable Tech Designed for Your Messiest Eats

CNW/ - SkipTheDishes, Canada's only homegrown food delivery network, is gearing up for another exciting NHL® season, delivering meals, groceries and more

SkipTheDishes has partnered with three of Canada’s top culinary talents, including Piano Piano’s head chef Victor Barry, for a new zero waste content series that provides educational resources ...

SkipTheDishes partners with Canadian chefs in food waste reduction initiative

SkipTheDishes partners with Circular Innovation Council and top Canadian chefs to encourage Canadians to reimagine their takeout leftovers and reduce food waste TORONTO, Oct. 2, 2023 /CNW/ - Each year, Canadians waste an astonishing 2.2 million tonnes of ...

Top Canadian Chefs Inspire Canadians to Eliminate Food Waste through Skip's Innovative Zero Waste Chef Campaign and Content Series

Canadians waste an astonishing 2.2 million tonnes of food, with 13% of that resulting from leftovers. For this year's Circular Economy Month and Waste Reduction Week (October 16-22), Canada's only homegrown food delivery network,

TORONTO, Sept. 25, 2023 /CNW/ - SkipTheDishes, Canada's only homegrown food delivery network, is giving Canadians another reason to be thankful this holiday season with the continued expansion of ...

Looking For New Ways To Give Thanks? Show Your Appreciation with SkipTheDishes Newest Gift Offering, Edible Arrangements!

SkipTheDishes, Canada's only homegrown food delivery network, is giving Canadians another reason to be thankful this holiday season with the continued expansion of their convenient gift and retail offerings across Canada!

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“Hiring data engineers is a piece of cake!” said no one ever. And for good reason. While it’s never been easy to recruit engineers—period, data engineers are a whole new ballgame. Despite the , hiring the backbone of your data team has never been more challenging. hypergrowth of the data engineering profession Why is it so hard to hire data engineers? In my opinion, it boils down to two key factors: : The data ecosystem is evolving fast, and more and more companies are betting big on data. We’ve gotten past the . This means that data teams are either training existing analysts and scientists to build and scale infrastructure or looking externally to fill these critical roles. Either way, if you slice it, there aren’t enough data engineers. The supply of data engineers doesn’t meet the demand COVID pause in hiring : The title “data engineer” is nascent, but the work of data engineering has been around much longer. Data scientists, analysts, database engineers, data architects, and back-end engineers have been handling the ingestion, transformation, testing, and infrastructure work required to build and scale data platforms for years; if you haven’t read , I highly recommend it. It’s hard to find the right candidates Maxime Beauchemin’s landmark article about this phenomenon Given the diversity of backgrounds that make up the data engineering mindshare, it can be tough to source the right candidates, and even more difficult for candidates to determine if they’re a good fit for your team based on the job description alone. Compensation aside, what matters most when it comes to recruiting great data engineers is whether or not joining your company will set them up for success, both in their careers and in the company itself. At the end of the day, a competitive salary and impressive logo may get your data engineers in the door, but it doesn’t mean they’ll stick around. In this blog post, we will cover 5 non-obvious ways (and one bonus way!) from some of the best data engineering leaders to attract and retain top talent. Measure your data engineer’s customer impact One of my biggest peeves with traditional engineering career ladders is that the promotions process tends to favor the bold—and . And by that, I mean, high visibility projects instead of behind-the-scenes or infrastructure work that is equally critical but often more time and resource-intensive than feature engineering, particularly when you’re on a lean team. not always for the better Similarly, data engineering work often goes unrecognized or is underappreciated compared to analytics or data science work. (If you haven’t read it yet, I highly encourage you to read this spicy and about this topic). Hacker News thread corresponding article How do you overcome this hurdle as a data engineering leader? It starts with customer impact. Before you even begin recruiting, identify tangible metrics that map data engineering work with its impact on your team’s internal customers. People want to work where their efforts are valued, and setting stakeholder and leadership alignment around the role data engineering plays in the broader data team’s strategy is critical. According to , Director of Platform Engineering, Big Data, at , setting benchmarks with your executive leadership entails not just a strong vision but also a way to tie the impact of the data platform and other data products to the company’s larger technology and business goals. Javier Pelayo Adidas One way to measure this? Metrics around reducing operational overhead and decreasing time and value for data products. “In the end, if you are providing any value and you are minimizing the operational overhead for analytics teams and other data users, it’s easy to quantify the value of data engineering to stakeholders and the broader company. From there, recognition and adoption of your team’s data products will follow.” Other suggestions for metrics to track the impact of your data engineers are as follows: How much time/money/resources do your data engineers save for the company? [ ] How does their work decrease time-to-value for significant data products? [ ] In what ways does their work impact larger engineering or analytic strategies? [ ] With these data points in place, you’re in a better position to highlight to prospective hires how data engineering work is prioritized, celebrated, and foundationally important to the success of your company. Ensure that your data engineers grow with the company Like many tech specialties, data engineering is evolving at a rapid pace. New tools and best practices are constantly emerging, and the best engineers will want to invest time in staying abreast of the latest developments and technologies. Also, just like any prospective new hire, data engineers will want to know how they’ll grow with your company. Currently, the onus is on data engineers to own more and more of the end-to-end data transformation workflow, from ingestion to modeling, requiring a broader understanding of the space and specialized skills. , Data Product Manager for Canadian food delivery marketplace (part of Just Eat Takeaway.com), is working with the data product and technology team to lead the charge on her team’s migration to decentralized data architecture, with embedded practitioners responsible for owning the entire data engineering workflow from end to end. Ebunoluwa Oke SkipTheDishes “Any data engineer we hire now and in the future would need to be able to do the end-to-end process of ingesting the data and also creating the data model because we want that engineer to understand the context of the data, not just in the sense of “oh this looks wrong,” but also, in the context of how the data is being used and who is using it,” she said. With this increased responsibility, it’s important that prospective hires know that their work will be acknowledged and tied directly to bottom-line KPIs and success metrics for the broader data team. For many companies, this isn’t the case. It’s no surprise that data engineers are often referred to as the “plumbers” of data science. An undoubtedly important and certainly under-appreciated role that—quite literally—keeps your pipe(line)s running. Investing in career development isn’t just about writing blog articles and speaking at conferences; it’s about setting your direct reports up with a career they can grow into as the team scales. Make the data ecosystem easy to navigate for data engineers These days, it’s cliché—and a P0—for businesses to call themselves “data-driven.” Experienced data engineers have likely seen firsthand that a company may talk a big game about data, but doesn’t actually put in the work to educate its employees on how to work with data products or even empower self-serve access to data. Without some foundational understanding of where to find, how to interpret, and how to troubleshoot basic data needs, internal data consumers can take up a significant amount of your data engineering team’s time with one-off requests or last-minute demands. And while providing guidance and overseeing governance is often part of the job description, the best and brightest data engineers will understand the difference between a company that talks the talk and a company that walks the walk by investing in data literacy and leveraging a data platform that enables contextual understanding across domains. , Data Platform Lead with Norwegian Public Roads, suggests that setting the stage for a strong culture of data discovery and accountability will set your new data engineers and analysts up for success once they get started. Lars Meisingseth “We’re in the process of developing and operationalizing the use of a social framework to discern what data sets are important and which ones aren’t as important, along various dimensions of “Who is using this? How often is it used? How often is this being used? What’s the value potential both internally and externally?” etc. to determine what data should be prioritized and shared across the organization and what could potentially be deprecated. This will also vary across domains and business use cases, but by leveraging a domain-oriented data platform, it will be easier to empower teams to make those judgment calls themselves.” This also helps foster a culture of valuing data—and when your data engineers work in an environment where their contributions are appreciated, they’re more likely to be satisfied and excited to grow alongside the rest of the team. Invest in tooling data engineers love Another common challenge? Using legacy or outdated solutions to try and build a “modern data stack.” Don’t get me wrong. You don’t have to be , , or to and use fancy tools. At its core, a data platform is just a central repository for all data, handling the collection, cleansing, transformation, and application of data to generate business insights. Airbnb Netflix Uber build a good data platform But this is only possible if you have the right technologies and processes in place to operationalize it at scale. In addition to the business case for building a data platform (increased innovation, competitive advantage, decentralized data ownership, scaling data analytics, etc.), there is a strong argument to be had when it comes to using your data platform and its underlying technologies as a hiring tool for data engineers. Recently, there’s been a lot of discussion around whether to go with open source or closed source solutions (the recent dialogue between and marketing teams really brings this to light) when it comes to building your data stack. Snowflake Databricks’ In my opinion, regardless of what side you take, both open-source (Apache Spark, Airflow, DBT, etc.) and best-in-class SaaS solutions (Snowflake, Redshift, Fivetran, Looker, Tableau, etc.) are excellent marketing tools for your company. In fact, most data engineering teams invest in a mix of both. At the end of the day, your practitioners want tools and skill sets that will: grow alongside their careers make their lives easier reduce complexity Choosing whether to go the open-source or SaaS route is rarely binary. When choosing a new solution or technology, the conversation should always seek to answer: “Is this the right tool for the job/my team.” Sometimes that’s open-source, and sometimes that’s SaaS—it really just depends on what you’re trying to achieve and what type of resources you’re willing to invest. Ensure your data engineers have autonomy A good data engineering leader will give their team the agency and influence to design, build, and scale projects in a way that interests them and pushes their technical and collaborative boundaries. Often, this boils down to ensuring that they feel ownership over what they’re working on and that what they’re working on is something that will advance their careers and increase their exposure to cross-functional teams across the company. , Head of Product, Data Platform at notes that good data engineering managers will give their team members work that not only interests them, but challenges them, too. Chad Sanderson Convoy, “If your data engineers are just running pipelines, that’s not very interesting work,” Sanderson said. “But if they’re building the systems and technologies that power the data platform, that’s much more compelling.” Encouraging autonomy starts with building a culture of trust in your team’s ability to own and execute projects delegated to them, as well as asking for their input on what they want to work on before they even join your company. By keeping the lines of communication open, proactively seeking feedback, and encouraging discussion even during the recruiting process, you can set the stage for a collaborative, trusting relationship moving forward, whether or not they choose to work at your company. The data world is small. You never know when you’ll cross paths next – heck, maybe they’ll even be your manager one day. Give your data engineers challenging projects This brings me to my next point: engineers are less likely to join your team if the work they’d be responsible for doesn’t challenge them, either on a technical or cultural level. Whether it’s rolling out a critical new feature, leading the charge on an RFC, or serving as point-of-contact for the implementation of a new tool, it’s critical to ensure that they’re pushing their professional boundaries. An important part of making sure that your engineers are working on projects that challenge and excite them? Automating rote or manual work, whenever possible. There’s a common saying among DevOps and Site Reliability Engineers ( , in many ways) that the goal of reliable systems is to . In the context of data engineering, manual toil might include fielding ad-hoc queries from downstream stakeholders, unit testing, data quality checks, and documentation. All of these activities, and many more, can be automated with the right approach. the predecessors of data engineers reduce manual toil As the Head of Data Engineering at an e-commerce company aptly told me, data engineers do not enter the field to test pipelines all day. “Engineers don’t love this work—it’s not the innovative problem-solving they probably got into the field to accomplish in the first place. It can be mundane to write use case tests every week, every time an API changes.” Senior or staff engineers in particular will find tremendous value in automation that allows them to spend their time and energy on designing, building, and innovating, rather than maintaining and troubleshooting existing systems. End-to-end data lineage, , and data discovery tools can help you build a more reliable and fault-tolerant data stack while reducing the toil of manual testing and documentation. Some solutions allow for both machine learning-generated observability and custom rule-setting, depending on your needs. data observability Other teams may choose to invest in automated data ingestion (i.e., Fivetran or Stitch) or augmented analytics. Whatever it takes to get your data engineers to focus on what will make an impact on their careers and professional fulfillment as opposed to mundane or repetitive. Hiring the next generation of data engineers Creating an environment that will draw in the best and brightest data engineers will give you the edge when it comes to recruiting and setting the stage for a thriving data organization for years to come. Building a data engineering team isn’t easy, particularly given how rapidly the industry is evolving, but in my opinion, that’s what makes this experience so rewarding—for managers and new hires alike.

5 Ways to Become a Leader That Data Engineers Will Love 

Meet the Winners of Startups in North and Central America

Qwest predicted Netflix, Hulu, and HBO GO in 1999. By Micah Rosenbloom , Partner urging people to temper the hype surrounding the Amazon/Whole Foods acquisition, essentially arguing that Amazon will be a disruptive player in the food industry, but don’t expect them to scarf down market share immediately. It’s an important point. Food represents 13% of US GDP and many of the deal’s boosters are underplaying the sheer volume of innovation in the food category at the moment. Erin Griffith wrote a great post QSRs like SweetGreen and Chipotle have redefined fast food while supermarkets like Whole Foods and Wegmans are increasingly doubling as cafeterias/restaurants with huge takeaway selections. Recent entrants in food delivery — Seamless Grubhub, Postmates, Uber Eats, etc. dramatically changed the ease and frequency of food delivery. We invested in Plated, Gobble, and SkipTheDishes (Acquired by JustEat), and with the exception of Uber, I’ve never seen companies grow revenues so quickly. In retrospect it makes sense, most of us eat every day, multiple times, but still, it’s astonishing that a group of companies that are less than ten years old now account for $10B in consumer spending. As Erin notes, many armchair analysts are beginning to write off the packaged meal category, as well as grocery stores more generally, based on the current Blue Apron share price, but let’s remember that this stuff takes awhile. The force of change in food has only just started. Change in Tech Takes Time. Lots of it. As a reminder, this commercial which premiered in 1999 at the height of the first dot-com boom. At the time, the promise of “every movie, ever made, in any language, day or night” was as fantastical as a flying car to most people. This was a time when Blockbuster, a store-sized version of Redbox popular in the last century, still issued fines to people who returned videotapes that weren’t rewound. Techies and VCs of course saw the potential and started building it, but nearly 20 years later, we’re still a ways away from enjoying that enticing value proposition. Sure, I can watch on any device that has an internet connection, but if I want to stream I need to sign into my Prime account. adds a third app, and yet another subscription fee to the mix. There is a cottage industry of websites , or , a given streaming service at any given time. A great many of our cultural treasures — and to name but two — remain in inaccessible IP vaults. Orange is the New Black The Americans Game of Thrones explaining which shows are coming to leaving ER The Fresh Prince of Bel-Air From a technologist’s point of view, video on demand is one of the simpler products to be built. It’s just packets over the internet, after all. But even in today’s world of widespread broadband, DRM, and omnipresent smart TVs and set top boxes, we’re still managing multiple services and remotes. How to think about self-driving cars, AI, and ecommerce Now imagine how long it may take one of today’s hot technologies, like self-driving cars, to take hold. If you read some pundits, they believe truck drivers will soon be as obsolete as fur trappers. Let’s examine this hypothesis critically. If it’s taken us 20 years to stream reruns, do you really think we’ll have technical solutions to route through Boston or SF’s backward traffic patterns, unexpected detours caused by exploding water mains, falling cranes, drunk drivers, and distracted pedestrians in short order? How quickly will the business case for fleet ownership be hammered out? Will 50 states be able to coalesce on laws governing the design of autonomous vehicles and licensure for the operators or coders in a timely fashion? Do we need new infrastructure, like sensors embedded in streets, to make fully autonomous driving work? Will progress be slowed the first time an AV clips a kindly grandmother crossing the road? Will the drivers of these trucks, who often pull double duty stocking shelves at their destinations, be replaced with robots? These are not problems that are going to be solved in the next year. It’ll probably take a decade or more. That’s why smart companies, who are able to play the long game, are investing in companies like Cruise today. Self-driving will only be a reality if companies are willing to do the hard work to make them a reality. Three’s Company For those panicking that AI will put most workers out of jobs, remember that Amazon’s vaunted machine learning recommendation engine is regularly a punchline in tweets because it advertises products that we’ve already purchased. Amazon has been grinding for over 20 years and ecommerce still only accounts for 10% of retail sales. Ebooks should have replaced dead trees by now, but paper has proven to be as resilient as Katniss Everdeen and their electronic counterparts have plateaued at 50% market share. Customers are weird and unpredictable. Regulation often routes technology in suboptimal directions. Established players can be surprisingly durable and loathe to adopt new technology. Food is not videos and is not AI and each will follow its own path, probably slower than you think. It’s fun to make bold proclamations about the future, but the perspective of history tells us we should be more circumspect. As Bill Gates said “We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten.”

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