Financial Data Analyst (Non-Healthcare)

Career Guide
Financial Data Analysts transform financial and operational data into insights that guide budgeting, forecasting, and decision-making. They build models and dashboards, validate data from multiple systems, and partner with finance and business teams to track performance and optimize results.

Key Responsibilities

  • Analyze revenue, cost, and margin trends using SQL/Excel
  • Build and maintain dashboards in Tableau or Power BI
  • Develop forecasts, budgets, and scenario models
  • Prepare monthly and quarterly performance reports and KPIs
  • Reconcile and validate data across ERP, CRM, and data warehouse
  • Partner with FP&A, accounting, and business teams to deliver insights

Career Progression

Can Lead To
Senior Financial Analyst
FP&A Manager
Finance Analytics Manager
Business Intelligence Manager (Finance)
Transition Opportunities
Business Intelligence Analyst
Risk Analyst
Product Analyst
Data Scientist (with upskilling)

Common Skill Gaps

Often Missing Skills
SQL at an intermediate level (CTEs, window functions)Financial modeling and forecasting (3-statement/driver-based)Dashboarding with Tableau or Power BIAccounting/GAAP and financial statement analysisPython or R for data cleaning and automation
Development Suggestions1) Take an intensive SQL + financial modeling course (e.g., FMVA plus SQL tutorials). 2) Build and publish two projects: a Power BI P&L dashboard and a Python-based forecast using SEC 10-K data.

Salary & Demand

Median Salary Range
Entry Level$65,000–$85,000
Mid Level$85,000–$110,000
Senior Level$110,000–$140,000
Growth Trend
growing — Data-driven FP&A and BI adoption expanding across industries.

Companies Hiring

Major Employers
JPMorgan Chase & Co.AmazonDeloitte
Industry Sectors
Financial ServicesTechnologyRetail & E-commerceConsulting & Professional Services

Recommended Next Steps

1
Earn a BI credential: Microsoft Power BI Data Analyst (PL-300) or Tableau Desktop Specialist.
2
Complete a structured modeling program (e.g., CFI FMVA or Wall Street Prep) and replicate a driver-based forecast.
3
Create a portfolio (GitHub/LinkedIn): revenue dashboard, cohort or pricing analysis, and a SQL case study; network via AFP/meetups and set 3–5 informational interviews with FP&A leaders.