Senior Analytics Engineer, Stash

• Experience: 5+ years in analytics engineering, data engineering (analytics-focused), or closely adjacent roles building production analytical data models. Evidence of Senior / L4-equivalent ownership of critical reporting domains
• Dbt & SQL craft: Advanced SQL and production-grade dimensional / mart modeling. Deep dbt experience (models, tests, sources, docs, incremental strategies, performance tradeoffs)—not “I’ve written a few models.”
• Warehouse experience: Hands-on with a modern cloud warehouse (Redshift, dbt, DataFold). Comfort debugging joins, grains, late-arriving data, and cost/runtime
• BI / semantic layer: Experience exposing trusted metrics in Looker and caring about naming, descriptions, and explore usability
• Quality mindset: You’ve owned data quality incidents—root cause, stakeholder communication, and durable fixes (tests, contracts, runbooks)—not just hotfixes
• Collaboration: Strong partnership with engineers, data scientists, and business stakeholders; able to negotiate definitions and push back when a request would create an unmaintainable model
• Programming: Solid Python for analysis, tooling, and light automation; Git/PR workflows are second nature
• Education: Bachelor’s in a quantitative or technical field, or equivalent experience
• AI fluency: Proven hands-on use of AI tools (e.g. Cursor, ChatGPT) in daily workflows, with strong judgment—validating outputs, adhering to Stash guidelines for sensitive data, and owning the quality of AI-assisted work

Desirable

• We’re looking for a Senior Analytics Engineer (Technical Level 4) to own and evolve the analytics foundation that powers Stash—our dbt-powered data mart, Looker semantic layer, and the quality systems that keep daily numbers trustworthy for Product, Growth, Finance, and Data Science
• You’ll sit at the intersection of data engineering and data science: production-grade SQL modeling, testing and freshness, clear metric definitions, and close partnership with stakeholders who depend on self-serve data
• Company bets (quality growth, Financial Advice, tier packaging, OKR visibility) all run through the mart—if models break, definitions drift, or sources go stale, the business loses trust. Your job is to make that trust durable
• Own core mart domains end-to-end: Design, build, and maintain production dbt models (bronze → silver → gold patterns in our data mart) for high-priority domains such as subscriptions, promotions/attribution, acquisition, and product usage
• Raise reliability and quality: Drive down recurring dbt test failures; add meaningful tests; document exceptions; partner on freshness SLAs and alerting so stale or wrong data is caught before Looker, OKRs, or DS models
• Keep Eng and the mart aligned: Partner with Backend / Product Engineering on instrumentation and schema changes (e.g., service migrations)
• Reconcile parity, get stakeholder sign-off, and cut over without silent metric breaks
• Enable Data Science and self-serve: Turn DS modeling requests into governed mart objects (grains, definitions, consumers). Build Looker explores/views and documentation so analysts and PMs can answer questions without waiting on a ticket for every pull
• Improve ops and performance: Contribute to mart job reliability (retries for transient failures, clear ownership of non-retryable logic failures). Profile and refactor high-cost models when reliability work is on track
• Partner across Data: Work with Data Engineering on upstream contracts and ingestion quality; with Data Science on measurement-ready datasets; with stakeholders on metric definitions that stick
• Raise the bar for the team: Mentor peers, review PRs for modeling and test quality, and use AI coding assistants productively while owning correctness—especially around financial and customer data