Financial Data Analyst

Key Responsibilities

• Ensure accuracy and consistency of financial data across: ERP (e.g., NetSuite), operational systems, data warehouse / BI tools.
• Reconcile partner reported revenue across SSPs, DSPs, and exchanges with internal reporting and analytics.
• Identify and resolve discrepancies between systems (e.g., revenue, cost, payments).
• Establish repeatable reconciliation frameworks (not ad hoc fixes).
• Partner with Data Engineering to: define financial data requirements, validate pipelines and transformations, ensure correct data mapping and lineage.
• Maintain and improve the financial system of record.
• Translate accounting logic into data models and rules.
• Define and maintain key financial metrics (revenue, gross profit, margins, etc.).
• Build datasets that support: executive reporting, product and operational analysis.
• Build dynamic, forecasting models that pull from historical data, update with new actuals, and support flexible scenario and assumption inputs.
• Support month-end close through: data validation, reconciliation automation.
• Reduce reliance on manual journal entries by improving upstream data quality.
• Implement data-level controls to prevent errors at the source.
• Ensure financial data is: auditable, traceable from source to reporting.
• Support audits with clear data documentation.
• Work closely with: Accounting, FP&A, Data Engineering, Product / Operations.
• Act as a translator between finance requirements and technical implementation.

Qualifications

• Bachelor's degree in Accounting, Finance, Data, or related field.
• 2–5+ years experience in: financial data analysis, business intelligence, or FP&A with strong data exposure.
• Strong SQL skills.
• Advanced Excel.
• Familiarity with ERP systems (NetSuite).
• Experience working with large datasets and high-volume environments (FinTech, Banking, Marketplaces, AdTech, or similar).
• Python - a plus.
• Experience with BI tools (Tableau, Looker, Power BI) - a plus.

Skills & Competencies

• Strong systems thinking (understands end-to-end data flows).
• Ability to identify root causes of data discrepancies.
• Detail-oriented with high standards for data accuracy.
• Automation mindset (eliminate manual work).
• Ability to communicate with both finance and engineering teams.