Source description
About the role
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).
More at WHOOP
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