Senior Data Engineer

Description

Who We Are:

The company is a fast-growing tech startup located in Winnipeg, Canada. The company uses AI to make business TV more fun, engaging, profitable, and easier to manage, starting with bars and restaurants. We've built proprietary tech that uses computer vision to watch live TV and improve the content with contextually-aware commercials, content, and games. Our tech also automates the manual AV tasks that venues have to do every day, to make bar management easier, standardized, and more profitable. Program alumni include Airbnb, Instacart, DoorDash, etc.

We are looking for a Senior Data Engineer to join our growing team!

Requirements

• The company runs on data from tens of thousands of devices in the field, the ads we serve, the venues and advertisers on the platform, and the output of our in-house computer vision models. We're hiring a Senior Data Engineer to own how that data is modeled, moved, and reported on. You'll standardize our data models, build and run the ETL pipelines that feed them, and own the reporting layer that engineering and the business rely on.
• This is a senior individual contributor role with real ownership from day one. You'll report to our Director of Software, Growth & Operations and work across every engineering team. The data function at the company is growing, and there will be plenty of room to grow with it as the team expands. The scope also reaches well beyond core data engineering. The foundation you build here will power product analytics across the company, the evaluation infrastructure behind our ML models, and a proprietary measurement and attribution system that shows advertisers exactly what their campaigns on the company drive.
• What You'll Be Doing:
• Standardize the data models that describe the company's business: devices, venues, advertisers, campaigns, impressions, and detection output, so every team works from the same definitions and trusts the result.
• Build and own the ETL pipelines that move data from our production systems and device fleet into the warehouse, with the orchestration, testing, and monitoring to keep them reliable as the fleet grows.
• Own the reporting and BI layer. Define the core metrics with each team, build the dashboards, and keep the numbers in them matching reality.
• Partner with engineering leads and the Head of Product to turn their questions into data they can act on.
• Set the standards for how data gets collected, named, and stored across the company, and work with the engineering teams to instrument their products at the source.
• Make sure the data models you build can feed production metrics and alerting across the fleet. The software teams own the alerting and on-call systems themselves, but they'll build on your foundation.
• Help shape what the data function needs next, whether that's tooling, infrastructure, or people, and make the case for it.
• Over time, extend the foundation into product analytics, evaluation tooling for our ML models, and proprietary campaign measurement and attribution.
• You've built a data function before, or been early enough at one to know what the first six months of decisions look like. You've seen how early choices about data models either pay off for years or get rebuilt, and you have opinions about which is which.
• You are hands-on. You write the SQL, you build the pipeline, you debug the job that failed. You'd rather build the thing than write a doc about building it.
• You think about data in terms of the questions it needs to answer. Before you build a table, you know who will use it and what decision it drives.
• You can work with people who aren't data people. You can sit with an engineering lead or a salesperson, figure out what they actually need, and translate that into something concrete.
• You hold a high bar for correctness. A dashboard that's wrong is worse than no dashboard, and you treat data quality as an engineering problem with tests and monitoring, not a cleanup task.
• You are comfortable with ambiguity. You'll set the direction for data at the company, and you want that kind of ownership.
• Your Qualifications:
• 6+ years of experience in data engineering, analytics engineering, or data science with a strong engineering component
• Experience designing data models and warehouse schemas that other teams build on
• Experience building and operating production ETL/ELT pipelines, including orchestration, testing, and monitoring
• Expert-level SQL and strong Python
• Hands-on experience with at least one modern data warehouse or query engine (ClickHouse, Athena, Snowflake, BigQuery, Redshift, or similar)
• Experience with BI and reporting tools (Metabase, Looker, Tableau, or similar) and defining company metrics
• Experience working with event data at scale, ideally from a large fleet of devices, a consumer product, or an ad platform
• Bachelor's degree in Computer Science, Engineering, Statistics, or a related field, or equivalent experience
• Willing to work in person at our Winnipeg office. We will support relocation for the right candidate.
• Experience with product analytics: experimentation, funnel and retention analysis, defining growth metrics
• Experience with ad tech data: impressions, attribution, measurement, or lift studies
• Experience building evaluation pipelines or ground truth datasets for ML models
• Experience hiring or leading a small data team
• Familiarity with AWS data services and DBT or a similar transformation framework

Benefits

• Employee stock options
• 100% employer-covered health benefits
• Flex-time & unlimited paid vacation
• Gym membership
• Catered lunches 3x per week
• Dog-friendly office
• Hybrid work model
• Free parking on-site
• Job Details:
• Job Type: Full-time
• Reports to: Director of Software, Growth & Operations
• Salary: $100,000 - $150,000 + stock options
• Schedule: Monday to Friday
• Ability to commute: can reliably commute to the office (required)
• Experience: 5+ years
• Visa: US citizen/visa only