Analytics Engineer, Data Platform

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

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.

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