Job description

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 - Amazing mission! Diversity and Inclusion: Diversity drives innovation, and our differences make us stronger. We‘re passionate about building a workplace that represents a variety of backgrounds, skills, and perspectives, and we do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. Everyone is welcome here! If there's anything we can do to support a disability or special need during your application or interview process, please email accommodations@upside.com. This email is for accessibility accommodations only, it should not be used to submit job applications. Notice To Recruiters And Placement Agencies: This is an in-house search with a dedicated recruiter. Please do not submit resumes to any person or email address at Upside. Upside is not liable for, and will not pay, placement fees for candidates submitted by any party or agency other than its approved recruitment partners.

Requirements

Must-haves

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 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

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

awssqlpythonci/cdmlaiaccessibility

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto

Parental Leave

Wellness