Financial Data Analyst
US$145,000 – US$180,000 / yr base salary
Posted 7 Aug 2026
Advertised as “Lead Financial Data Analyst”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 7+ yrs
- Management level
- Individual contributor
- Employment
- Hybrid
- Contract
- Full-time
| Base salary | US$145,000 – US$180,000 / yr |
|---|
Meet Upside:
We created Upside to transform brick-and-mortar commerce. Our technology uses the sophistication of online retail—profit measurement, attribution, and incrementality—to provide users with more value on their everyday purchases and brick-and-mortar businesses with new, profitable customers. We’ve helped millions of users earn 2 to 3 times more cashback than any other product, and hundreds of thousands of brick-and-mortar businesses earn measurable profit. Billions of dollars in commerce run through the Upside platform every year, and that value goes directly back to our retailer partners, the consumers they serve, and important sustainability initiatives.
The Role:
As a Lead Financial Data Analyst, you will own the translation of large operational datasets into strategic financial frameworks. Sitting at the intersection of FP&A and Data Engineering, you will scale our financial data infrastructure, replace legacy reporting with automated pipelines, and deliver high-impact analytics directly to senior leadership and cross-functional business partners.
What You’ll Do:
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Scale Data Architecture: Partner directly with Data Engineering to design, optimize, and maintain financial data warehouses (using Snowflake).
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Owner of Financial Datasets: Define, write, and maintain complex SQL queries and dbt models to transform raw data into clean, audit-ready financial metrics.
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Maintain FP&A Planning Tool: Manage spend forecast and actuals data for department budget owners in planning tool (Pigment) as well as drive optimization of spend through AI exploration and trend analysis.
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Automate Revenue Workflows: Architect automated pipelines to track key user metrics such as CAC, LTV, Payback periods, and IRR.
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Statistical Forecasting: Leverage Python and AI to improve user cohort performance forecasting as well as merchant net revenue retention.
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New Vertical Pricing Support: Partner with Product and Go-to-market teams to model the financial impact of new pricing models, monetization strategies, or international expansions.
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Institutional Dashboarding: Design and build self-service dashboards (using Looker, HEX) that allow executive leadership to track real-time performance of the business.
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Ad-Hoc Strategic Analytics: Act as analytical swat team for high-stakes business cases, evaluating corporate development opportunities or M&A modeling.