Senior Analytics Engineer (Finance) at WHOOP
Job Description
At WHOOP, we're on a mission to unlock human performance and healthspan. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives.
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.
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 HAVE
- 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).