Finance Data Product Analyst — FP&A

Intuitive•Sunnyvale, GA

About The Position

The Finance Data Product Analyst serves as dedicated FP&A analytics support, owning the requirements-to-handoff lifecycle across core FP&A domains. The role fills a gap in dedicated capacity for FP&A requirements gathering, stakeholder coordination, and handoff from AI/analytics work to engineering. It supports GLTD, Revenue, CapEx, and headcount portfolios, with the goals of faster turnaround, stronger domain ownership, and fewer downstream rework cycles. The analyst gathers and clarifies Finance requirements, translates them into technical specifications and data-model requirements, and ensures those specifications are validated before development. The role also supports data validation, reconciliation, UAT, investigation of existing EDW models, coordination between onsite and offshore teams, and documentation. It contributes to adoption of AI-augmented analytics by identifying business needs that may benefit from AI/ML and turning them into actionable requirements.

Requirements

At least 4 years of experience in BI analysis, data product analysis, or finance analytics support roles.
Strong understanding of FP&A concepts, including general ledger, revenue recognition, CapEx planning, headcount reporting, and cost center hierarchies.
SQL proficiency for data investigation, querying, tracing, and validating data across EDW layers; building pipelines is not expected.
Experience gathering and documenting requirements and translating business needs into technical specifications.
Familiarity with Snowflake or similar cloud data platforms from an analyst or consumer perspective.
Experience with Tableau or similar BI tools for report validation and basic analysis.
Strong written and verbal communication skills, including the ability to write clear specifications and coordinate across time zones with onsite teams.
Detail-oriented, structured approach to tracking deliverables and handoff checklists.
Bachelor's degree in finance, computer science, mathematics, information systems, or business.

Nice To Haves

SAP Finance or ERP data familiarity, including GL, AP, AR, and cost center structures.
Exposure to AI/ML concepts sufficient to recognize where AI-assisted analytics could add value to FP&A workflows.
Experience working in offshore delivery models with structured SDLC or agile processes.
Knowledge of data governance and data quality practices.

Responsibilities

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 and validate that specifications are complete and unambiguous before development.
Perform data validation, reconciliation, and UAT support to 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 by managing requirements backlogs, tracking delivery status, flagging gaps or conflicts, and ensuring timely handoffs.
Maintain business requirements documents, data dictionaries, test cases, and process guides.
Support AI-augmented analytics adoption by identifying business needs that may benefit from AI/ML capabilities and translating them into actionable requirements.