layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

Analytics Engineer, Data Platform at Upside | LAYIQ

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.

Analytics Engineer, Data Platform

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