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Stash is democratizing wealth creation through education, advice, and financial products. We're seeking a Senior Analytics Engineer (Technical Level 4) to own and evolve the analytics foundation powering Stash—a dbt-powered data mart, Looker semantic layer, and quality systems that keep daily numbers trustworthy for Product, Growth, Finance, and Data Science teams.
You'll sit at the intersection of data engineering and analytics, responsible for production-grade SQL modeling, testing, freshness guarantees, and clear metric definitions. Company bets around quality growth, Financial Advice, tier packaging, and OKR visibility all depend on the mart. Your job is to make that trust durable and scalable.
Key responsibilities include:
- Own core mart domains end-to-end: design, build, and maintain production dbt models (bronze → silver → gold patterns) for high-priority domains such as subscriptions, promotions/attribution, acquisition, and product usage.
- Raise reliability and quality by driving down dbt test failures, adding meaningful tests, documenting exceptions, and partnering on freshness SLAs and alerting.
- Keep Engineering and the mart aligned by partnering with Backend/Product Engineering on instrumentation and schema changes, reconciling parity, and cutting over without silent metric breaks.
- Enable Data Science and self-serve analytics by turning modeling requests into governed mart objects and building Looker explores/views with clear documentation.
- Improve operational performance by contributing to mart job reliability and refactoring high-cost models.
- Partner across Data teams on upstream contracts, ingestion quality, and metric definitions.
- Mentor peers, review PRs for modeling and test quality, and use AI coding assistants productively while owning correctness.
Required qualifications: 5+ years in analytics engineering, data engineering (analytics-focused), or adjacent roles building production analytical data models. Advanced SQL and production-grade dimensional/mart modeling expertise. Deep dbt experience (models, tests, sources, docs, incremental strategies). Hands-on experience with modern cloud warehouses (Redshift preferred). Experience exposing trusted metrics in Looker. Proven ownership of data quality incidents with durable fixes. Strong collaboration skills with engineers, data scientists, and business stakeholders. Solid Python for analysis and tooling. Bachelor's in a quantitative or technical field or equivalent. Proven hands-on use of AI tools (Cursor, ChatGPT) with strong judgment.
Gold stars: fintech or regulated environment experience, familiarity with Airflow/orchestration, Spark, or Fivetran-style ingestion, Mixpanel/Segment event modeling experience.
