Finance Data Product Analyst — FP&A

Intuitive•Durham, NC

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 addresses a gap in requirements gathering, stakeholder coordination, and AI-to-engineering handoff, with the goal of improving turnaround, strengthening domain ownership across GLTD, Revenue, CapEx, and HC portfolios, and reducing downstream rework. The analyst gathers and documents business needs, translates them into technical specifications and data-model requirements, and coordinates their handoff to analysts and engineering. The role also supports data validation, reconciliation, user acceptance testing, investigation of existing EDW models, delivery tracking across onsite and offshore teams, documentation, and adoption of AI-augmented analytics.

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 investigating, tracing, and validating data across EDW layers; pipeline development is not expected.
Experience gathering and documenting requirements and translating between business and technical teams.
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 writing clear specifications and coordinating across time zones with onsite teams.
Detail-oriented and structured approach to tracking deliverables and handoff checklists.
Bachelor's degree in finance, computer science, mathematics, information systems, or business.

Nice To Haves

Familiarity with SAP Finance or ERP data, 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 Finance stakeholder requirements across GLTD, Revenue, CapEx, and HC portfolios.
Translate business requirements into technical specifications and data-model requirements for handoff to the onsite senior analyst and engineering team.
Own and validate the requirements-to-engineering handoff to ensure specifications are complete and unambiguous and to reduce rework.
Perform data validation and reconciliation, and support user acceptance testing to confirm 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 onsite and offshore teams, manage requirements backlogs, track delivery status, flag gaps or conflicts, and ensure timely handoffs.
Maintain business requirements documents, data dictionaries, test cases, and process guides.
Identify business needs that could benefit from AI/ML capabilities and translate them into actionable requirements.