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.

The work

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 MarTech, so a lot of the job is turning a messy question into something concrete and trustworthy. Team of six. Snowflake, dbt, Dagster, AWS.

What you'll do in your first year

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 might be a good fit if

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

Nice to have, not required

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 warehouse design

Supporting ML workflows, like building features or watching model inputs

Making warehouse data usable by AI tooling; semantic layers, data contracts, or documentation that agents and humans can both read

One note on the lists above: they describe the work, not a checklist you have to clear. Plenty of strong people talk themselves out of applying over one missing bullet. If you meet most of the core list and this sounds like your kind of problem, apply and let us decide together.

Benefits

Medical, dental, and vision coverage starting on Day 1

Equity (ISOs)

401(k) program

Family planning programs + paid parental leave

Physical fitness and wellness memberships

Emotional and mental health support programs

Unlimited PTO + 10 paid federal holidays + our annual, week-long Winter Break

Flexible work environment

Lunch reimbursement for in-office employees

Employee Resource Groups

Learning and Development stipend

Transparent culture