Staff Finance Data Analyst

7+ years in analytics or data science roles focused on Finance (SaaS or usage‑based preferred)
Deep hands‑on expertise with dbt (production‑grade models, testing, CI/CD) and Snowflake (performance tuning, RBAC, cost management)
Advanced SQL + Python (pandas, scipy/statsmodels, or Prophet) for forecasting, scenario modeling, and statistical analysis
Strong grasp of SaaS / cloud unit economics—MRR/ARR, COGS allocation, gross margin, payback, LTV/CAC, cohort retention
Demonstrated success building usage‑based pricing and forecasting frameworks from raw event data
Comfortable communicating insight to executive audiences and drilling into data with engineers
Experience with BI / notebook tooling (Looker, Hex, Mode, Tableau, or similar) and data transformation tools like dbt
Bachelor’s or Master’s in Finance, Economics, Statistics, Data Science, or a related field

Model usage‑based economics – Build and maintain dbt models in Snowflake that translate raw product telemetry into ARR, margin, and unit‑economics views
Forecast revenue & costs – Design Python‑driven Monte Carlo and statistical models to project usage, revenue, and gross margin under multiple scenarios
Run variance & root‑cause analyses – Explain plan‑vs‑actual deltas across revenue, COGS, and operating expense lines; surface the drivers that matter
Simulate pricing & packaging changes – Partner with Product Monetization to quantify impact of new tiers, overage bands, and feature gates
Automate financial reporting – Deliver self‑serve dashboards (e.g., Hex, Mode, Looker) that Finance, ELT, and Board stakeholders trust every month‑end close
Elevate data quality – Define testing, reconciliation, and data‑quality SLAs so Finance can audit every revenue and cost metric back to source
Mentor analysts & engineers – Provide best‑practice guidance on dbt design patterns, Snowflake optimization, and Python analytical tooling

No preferred qualifications provided.