Senior Analytics Engineer

Responsibilities

• Design, build, and maintain the dbt transformation layer that standardizes financial actuals (revenue, costs, headcount, operational metrics) from source systems into model-ready datasets.
• Develop Snowflake-native functions, stored procedures, and frameworks that serve as the computational engine for the financial model — including allocation logic, driver-based calculations, and scenario parameterization.
• Partner with FP&A to translate complex financial modeling logic (budgets, forecasts, variance analysis, cohort-level P&L) into maintainable, version-controlled SQL and dbt models.
• Build and maintain data integrations between financial source systems (NetSuite, Stripe, payroll, billing) and the Snowflake warehouse, collaborating with Data Engineering on ingestion reliability.
• Implement comprehensive data quality testing frameworks — ensuring actuals tie to source systems and that downstream model outputs are auditable and trustworthy.
• Support the FP&A team's use of Sigma as the model interface layer, ensuring clean handoff points between the dbt/Snowflake backend and the Sigma workbook frontend (input tables, formulas, parameterized views).
• Document the model architecture, assumptions, and data lineage so that the system is transparent and maintainable beyond a single person.
• Apply software engineering best practices to all analytics code: version control (Git), modular design, CI/CD, and peer review.
• Proactively identify opportunities to improve model performance, reduce refresh latency, and scale the system as WHOOP's financial complexity grows.
• Design and implement AI-augmented financial workflows using Snowflake Cortex ML functions — including automated forecasting, anomaly detection for variance analysis, and contribution analysis for driver decomposition.
• Build LLM-powered automation where appropriate (e.g., auto-generated variance commentary, natural language model interrogation via Cortex Analyst / Snowflake CoWork).

Qualifications

• 4-7 years of experience in analytics engineering, data engineering, or a technical finance/BI role with hands-on ownership of production dbt projects.
• Expert-level SQL — comfortable writing complex window functions, recursive CTEs, UDFs, and stored procedures in Snowflake.
• Deep dbt experience: sophisticated projects, custom macros, testing strategies, incremental models, and documentation-as-code.
• Strong understanding of financial data concepts: chart of accounts structure, revenue recognition, cost allocation, budget vs. actual reconciliation, and driver-based modeling.
• Experience designing data models that support parameterized analysis (scenarios, sensitivities, what-if calculations) — not just static reporting.
• Familiarity with ERP and financial systems (NetSuite, Stripe, or similar) and the data integration challenges they present.
• Ability to work autonomously with FP&A stakeholders — translating ambiguous business requirements into precise technical specifications without heavy project management overhead.
• Strong opinions on data modeling best practices, loosely held — comfortable advocating for the right design while adapting to business constraints.

Nice-to-Haves

• Experience with Sigma Computing (workbooks, input tables, materialization, calculated columns) or a similar spreadsheet-over-warehouse BI tool.
• Python or Snowpark for more complex transformations or automation.
• Experience with Snowflake ML functions (FORECAST, ANOMALY_DETECTION, TOP_INSIGHTS) or equivalent time-series / statistical tooling.
• Familiarity with LLM-based automation (prompt engineering, structured outputs, Snowflake Cortex AI functions).
• Interest in AI-augmented finance — someone who sees the financial model as a living system that should get smarter over time, not just a static set of tables.
• Prior experience building financial models or FP&A systems specifically (vs. general analytics engineering).
• Exposure to subscription/SaaS business metrics (LTV, CAC, cohort retention, MRR/ARR).