Senior Analytics Engineer, WHOOP

• 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
• 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

Desirable

• We are hiring a Senior Analytics Engineer (Finance) to build and own the data infrastructure powering WHOOP's financial planning model
• This role sits at the intersection of Analytics Engineering and FP&A — you will design the foundational data layer that enables financial forecasting, scenario modeling, and actuals reconciliation at scale
• You will build the engine that makes analysis possible: standardizing financial actuals in Snowflake, developing reusable dbt frameworks, and creating the Snowflake-native functions and structures that power the model's logic at scale
• This is a builder role
• You'll partner directly with FP&A to translate financial modeling requirements into scalable, tested, and well-documented data products — and ensure the system remains reliable and extensible as the business grows
• 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)