Analytics Engineer, Data Platform
$149,000 - $180,000 per year
Apply (opens in a new tab)Analytics Engineer, Data Platform
Posted yesterday
SENIORITY
Senior
SALARY
$149,000 - $180,000 per year
About the role
- In Your First YearOwn 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 upAdd monitoring and alerting to your models so your team catches problems before stakeholders doTake 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
- 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 orchestratorCI/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
- Medical, dental, and vision coverage starting on Day 1Equity (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!
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
