Sr. Finance Analyst - FP&A

What you'll do

• Automate and industrialize finance workflows by converting manual Excel-based processes into reliable, scalable automated pipelines and self-serve tools
• Develop and operationalize statistical and machine learning models to improve forecast precision, balancing accuracy and explainability
• Create internal FP&A tools that make analyses repeatable, auditable, and easy to consume by Finance leadership and business partners
• Partner cross-functionally with Finance teams and technical partners to identify friction, quantify impact, and implement more efficient ways of operating
• Design and maintain rule-based engines and data pipelines that support forecasting, reporting, and decision workflows
• Translate analyses and automations into clear narratives and meaningful visuals; communicate recommendations confidently internally
• Use LLM-powered development workflows (e.g., Claude Code) to accelerate delivery while maintaining quality, security, and auditability

Requirements

• Strong SQL skills (writing, debugging, and optimizing queries against large datasets)
• Advanced Excel skills and fluency in common FP&A workflows (structured modeling, reconciliation, scenario analysis)
• Working Python literacy (ability to write production-quality scripts and notebooks; comfort with packaging, testing, and reproducibility)
• Experience working in a Git-based workflow (branching, pull requests/code review, version control hygiene)
• Proven ability to take ambiguous problems and drive them to outcomes, identifying opportunities, scoping work, executing, and iterating
• Comfort operating in a role with direct financial impact, including attention to controls, data quality, and auditability
• Excellent communication skills (written and verbal), including the ability to tailor technical detail to the audience
• 2 - 4 years working experience in Analytics, Finance, or AdTech (preferred)
• Experience with Python data and visualization tooling such as pandas and plotly (preferred)
• Experience building lightweight applications for analysts and business users (e.g., Streamlit, Dash, or similar) (preferred)
• Familiarity with distributed processing and large-scale data (e.g., PySpark) (preferred)
• Experience implementing agentic workflows and/or automation in a business context (preferred)
• Comfort using LLM tools (e.g., Claude Code) to speed up development, analysis, documentation, and iteration (preferred)
• Exposure to programmatic advertising / AdTech concepts (forecasting, seasonality, supply dynamics, fees/take rates, identity, etc.) (preferred)

Tech stack

Benefits