Analytics Engineer, Data Platform

Meet Upside

We created Upside to transform brick-and-mortar commerce. Our technology uses the sophistication of online retail—profit measurement, attribution, and incrementality to provide users with more value on their everyday purchases and brick-and-mortar businesses with new, profitable customers. We’ve helped millions of users earn 2 to 3 times more cashback than any other product, and hundreds of thousands of brick-and-mortar businesses earn measurable profit.

Billions of dollars in commerce run through the Upside platform every year, and that value goes directly back to our retailer partners, the consumers they serve, and important sustainability initiatives.

We created Upside to transform brick-and-mortar commerce. Our technology uses the sophistication of online retail—profit measurement, attribution, and incrementality to provide users with more value on their everyday purchases and brick-and-mortar businesses with new, profitable customers. We’ve helped millions of users earn 2 to 3 times more cashback than any other product, and hundreds of thousands of brick-and-mortar businesses earn measurable profit.

Billions of dollars in commerce run through the Upside platform every year, and that value goes directly back to our retailer partners, the consumers they serve, and important sustainability initiatives.

Five million people use Upside to earn cash back on gas, groceries, and dining. The offers they see, the lifecycle messages they get, and the partner launches behind both all run on data models. You'll own a set of those. Not just building them. Deciding how they should be shaped, testing them, monitoring them, and documenting them well enough that someone else can safely build on your work.

You'll sit close to Marketing, Data, and Mar Tech, so a lot of the job is turning a messy question into something concrete and trustworthy. Team of six. Snowflake, dbt, Dagster, AWS.

• Own a scoped domain of dbt models: design, build, test, ship, and monitor them, with a clear point of view on how they should be structured
• Turn ambiguous asks from Marketing and Product into scoped work, and talk openly about tradeoffs when the ask and the timeline don't fit together
• Write the design doc for the features you own and break the work into pieces teammates can pick up
• Add monitoring and alerting to your models so your team catches problems before stakeholders do
• Take your turn on our support rotation, debug what breaks, and prevent the repeat
• Leave behind runbooks, schema docs, and diagrams that make your work easy for the next person to own
• Coach engineers earlier in their careers on the team, in code review and day to day

• You've spent around 3–5 years in data or analytics engineering, or you've done comparable work under a different title
• You're fluent in SQL, comfortable with window functions and complex joins, and you think about query performance without being asked
• You've owned dbt models in a version-controlled repo; conventions, tests, CI, and the occasional cleanup of someone else's tangle
• You know Python well enough to work in orchestration, transformations, and tests
• You have an opinion on modeling tradeoffs (dimensional vs. one big table) and can explain which you'd pick and why
• You can explain a technical decision to a marketer and an engineer in the same meeting, and adjust how you say it for each
• You've worked in Snowflake, or a comparable warehouse you could translate from

• Marketing, growth, or lifecycle data: events, attribution, experimentation, or tools like Braze, Iterable, or Segment
• Dagster, Airflow, or another modern orchestrator
• CI/CD for data, data governance, or cost-conscious…