We're looking for a Senior Analytics Engineer to lead the data architecture behind our most business-critical financial work: billing, regulatory reporting (), compliance, financial forecasting and executive reporting. You'll design the data solutions that finance, payments and audit teams depend on, set the standards for how analytics work ships, and mentor others on the team.

This is a shape-the-team role. You'll have real influence over how we work, what we build and the pace we move at.

We expect you to use AI fluently where it adds leverage in modeling, code review and debugging, and to apply clear judgment on where it doesn't.

Responsibilities

Lead the architecture and development of high-impact data models in dbt.
Architect data solutions across business-critical financial workflows, including billing, regulatory reporting (), compliance, financial forecasting and executive reporting.
Mentor analytics engineers and review code to uphold standards across the team.
Find and fix performance bottlenecks across the data stack.
Translate business problems into clean, efficient data solutions in close partnership with stakeholders.
Set team-wide standards for documentation, CI/CD, observability and testing.
Set the team's norms for where AI belongs in our workflow, from model development to code review, and where human oversight stays non-negotiable.

Requirements

5+ years in analytics engineering or a similar role, with a track record of designing data models that hold up to cross-functional use.
Deep, hands-on expertise with dbt, SQL and Python on a modern data platform (Databricks or equivalent).
Strong grasp of pipeline orchestration, testing frameworks and the practices that keep them healthy at scale.
Comfortable setting technical direction, mentoring engineers and working closely with finance, payments and audit stakeholders. You can explain a trade-off clearly and influence the decision that follows.
You use AI hands-on in your day-to-day technical work and can show specific examples of how it has changed the way you build, review or debug data models.
You catch AI's errors before anything ships, including hallucinations, overconfident answers and plausible-but-wrong code, and you have a clear sense of where human oversight stays non-negotiable.

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