Senior Analytics Engineer (Finance)

Whoop•Boston, MA

5d•$150,000 - $215,000

About The Position

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.

Requirements

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).

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).