The workFive 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
- Amazing mission!
