Finance Data Product Analyst — FP&A

Primary Function of Position

The Finance Data Product Analyst will serve as the dedicated FP&A analytics support resource, owning the requirements-to-handoff lifecycle across core FP&A domains. This role fills a current gap where FP&A requirements gathering, stakeholder coordination, and AI-to-engineering handoff are absorbed across leads without dedicated capacity. The analyst will ensure faster turnaround, stronger domain ownership across GLTD, Revenue, CapEx, and HC portfolios, and less rework in downstream deliverables.

Roles and Responsibilities

Serve as the dedicated FP&A analyst for requirements intake — gather, clarify, document, and prioritize business requirements from Finance stakeholders across GLTD, Revenue, CapEx, and HC portfolios.

Translate business requirements into structured technical specifications and data model requirements for handoff to the onsite senior analyst and engineering team.

Own the requirements-to-engineering handoff process — ensure specifications are complete, unambiguous, and validated before passing to development, reducing rework cycles.

Perform data validation, reconciliation, and UAT support — verify that delivered models and reports match business intent and source data.

Debug and trace data through existing EDW models to answer stakeholder questions, identify discrepancies, and document root causes for engineering resolution.

Coordinate between onsite and offshore teams — manage requirements backlogs, track delivery status, flag gaps or conflicts, and ensure timely handoff at each stage.

Maintain documentation: business requirements documents, data dictionaries, test cases, and process guides.

Support AI-augmented analytics adoption by articulating business needs that could benefit from AI/ML capabilities and translating them into actionable requirements.

Qualifications

Required Skills and Experience

4 years of experience in BI analysis, data product analysis, or finance analytics support roles.

Strong understanding of FP&A concepts — general ledger, revenue recognition, CapEx planning, headcount reporting, cost center hierarchies.

SQL proficiency for data investigation — ability to query, trace, and validate data across EDW layers (not expected to build pipelines).