Who we are
About Stripe
Stripe is a financial infrastructure platform for businesses. Millions of companies - from the world’s largest enterprises to the most ambitious startups - use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career.
About the team
Stripe's Finance & Strategy team is hiring a Finance Analytics Analyst. This role requires a finance professional with strong FP&A capabilities, technical data skills (SQL, BI tools), and demonstrated AI adaptability. The individual will own financial planning, variance analysis, and P&L reporting for assigned business areas while using data and technology to deliver high-quality financial insights. This role reports into the Finance & Strategy Center of Excellence.
Responsibilities
Financial Planning & Analysis
- Own P&L reporting and analysis for assigned product lines, business units, or functional cost centers
- Lead monthly and quarterly planning, forecasting, and close processes
- Perform variance analysis (actuals vs. budget, actuals vs. forecast, period-over-period) with clear identification of root causes and business drivers
- Track and reconcile accruals vs. actuals; ensure accuracy and completeness in financial reporting
- Support annual budgeting, long-range planning, and scenario modeling
- Build and maintain financial models for business case analysis, investment decisions, and resource allocation
- Partner with business teams to provide financial perspective on operational and strategic decisions
Data Analytics & Reporting
- Write SQL queries to extract, validate, and analyze data from financial systems and data warehouses
- Build and maintain financial dashboards and reports using Power BI, Tableau, or equivalent BI tools
- Develop data models that integrate financial and operational data for planning, forecasting, and performance reporting
- Automate and standardize recurring reports to reduce manual effort and improve consistency
- Navigate complex, high-volume data environments (trillions of transactions across products and geographies) to extract relevant financial signals
- Ensure data accuracy and consistency across all financial outputs
AI-Augmented Working
- Use AI tools (Claude, Cursor, Claude Code, and similar platforms) as part of workflow for analysis, coding, report building, dashboard prototyping, and problem-solving
- Apply an AI-first approach to new tasks, evaluate where AI can accelerate delivery before defaulting to manual methods
- Contribute to team-wide AI adoption by sharing effective workflows, prompts, and use cases
Business Insights & Communication
- Prepare executive-level reports on financial performance, trends, and key metrics with clear conclusions and recommendations
- Conduct ad-hoc analysis to support strategic initiatives and leadership decision-making
- Translate data and financial analysis into actionable business recommendations for finance and non-finance stakeholders
- Track and interpret key financial and operational metrics including revenue, processing volume, take rate, costs, margins, and unit economics
Requirements
Financial & Business Skills
- 5–7+ years of experience in FP&A, financial analysis, strategic finance, or corporate finance roles
- Strong understanding of financial statements (P&L, balance sheet, cash flow) and the ability to connect them into a complete financial picture
- Demonstrated experience in budgeting, forecasting, variance analysis, and financial planning cycles
- Working knowledge of accrual accounting, revenue recognition, cost allocation, and reconciliation processes
- Fluency in financial metrics: revenue, gross margin, EBITDA, operating leverage, unit economics
Technical & Data Skills
- Must Have: Strong SQL skills for data extraction, transformation, and analysis from relational databases and data warehouses
- Must Have: Proficiency in Power BI, Tableau, or equivalent BI tools for dashboard development and reporting
- Must Have: Advanced Excel / Google Sheets — financial modeling, scenario analysis, pivot tables, complex formulas
- Preferred: Python or R for data analysis, automation, or analytical workflows
- Ability to work with large, complex datasets across multiple data sources
AI Adaptability
- Must Have: Active, demonstrable use of AI tools in current work — candidates should be able to provide specific examples of how AI has improved their workflow, output quality, or speed
- Willingness and ability to continuously adopt new AI tools and methods as they become available
- Comfort using AI for coding assistance, data exploration, dashboard prototyping, and analytical problem-solving
Mindset & Working Approach
- Ownership: Treats assigned areas as personal accountability — does not wait for direction to investigate issues, identify risks, or surface insights
- Depth and curiosity: Goes beyond surface-level analysis to understand business context, root causes, and second-order implications
- Quality and accuracy: Holds a high personal standard for the correctness, completeness, and trustworthiness of all outputs
- Business context orientation: Understands the business model and connects financial data to what is actually happening operationally and strategically
- Clear communication: Presents financial and analytical findings to diverse audiences (finance, product, engineering, leadership) with clarity and precision
- Speed with rigor: Operates effectively in a fast-paced environment without compromising accuracy
Preferred Experience
- Background in technology, payments, fintech, SaaS, or high-growth platform businesses
- Experience with ERP systems (NetSuite, SAP, Oracle) or planning tools (Anaplan, Adaptive Insights, Pigment)
- Exposure to data warehousing concepts and cloud data platforms (Snowflake, BigQuery, Redshift)
- Experience in cross-functional environments working directly with product, engineering, and go-to-market teams
- Experience with high transaction volume environments where data complexity is inherent