Data Governance Lead

Location : Mountain view CA (onsite)

The Role

Reporting to the Director, Finance Data, Automation and AI, the Principal, Finance Data Governance & Strategy will own the data foundation that Intuit's Finance transformation is built on. As Finance modernizes its planning, reporting, and ERP platforms under the Unified Plan to Perform program, and as AI moves from pilots into production Finance workflows, the value of that work depends on data that is defined once, owned clearly, traceable to source, and safe to use.

This is a senior individual contributor role for someone who is equally comfortable setting policy and building the technical foundation that makes it real. You will stand up and scale Finance's governance and stewardship model, ensure governance is designed into our platform modernization rather than retrofitted afterward, and lead emerging capabilities such as synthetic data that let Finance test, develop, and train models without exposing sensitive data.

The role sits at the intersection of control and innovation. You will partner across Finance, Technology, Enterprise Data, Risk and Compliance, Legal, and Security, and represent Finance in shaping Intuit's enterprise data strategy

Responsibilities

• Own Finance's data governance framework: Define and operate data definitions, quality standards, stewardship, and access controls across Finance data domains, with accountable owners and clear decision rights for every critical dataset.
• Build lineage and the Finance data map: Establish and maintain a living inventory of Finance data sources, systems, flows, and transformations, so every metric traces back to source and the map serves as the reference for access, risk, audit, and architecture decisions.
• Embed governance in platform modernization: Lead the data governance, mapping, and reconciliation strategy for Finance's ERP and planning platform migration, ensuring ownership, lineage, and SOX-relevant controls are re-established in the target state rather than replicated as-is.
• Lead Finance's synthetic data strategy: Define where synthetic data can replace production data for testing, development, model training, and analytics; set fidelity and validation standards; and partner with Engineering and AI/ML teams on reusable pipelines and tooling.
• Design controls with partners: Work with Technology, Enterprise Data, Risk and Compliance, Legal, and Security on access controls and sensitive-data handling that meet SOX and privacy requirements without slowing the business.
• Represent Finance in enterprise data strategy: Sit on enterprise data governance forums, align Finance standards with Intuit-wide policy, and ensure Finance's needs are reflected in company-wide data decisions.
• Shape what comes next: Build and own the roadmap for Finance's data capability maturity, evaluating emerging capabilities (AI-driven data quality, automated lineage, federated access) and acting as a thought partner to Finance leadership on where to invest.

Qualifications

Qualifications

• 8+ years in data governance, data strategy, or data architecture, including 2 to 3 years operating at a principal or strategic level.
• Demonstrated experience standing up or scaling a data governance and stewardship program.
• Experience governing data through a large legacy-to-cloud platform migration, with a focus on lineage, ownership, and controls rather than technical migration execution alone.
• Experience building or owning enterprise data maps, catalogs, and lineage tooling.
• Strong technical fluency, including SQL and modern data warehouse environments (Snowflake, BigQuery, or similar).
• Experience in a regulated or controls-sensitive environment (SOX/ICFR exposure preferred).
• Track record of influencing enterprise-level data strategy and senior stakeholders across business and technology.
• Bachelor's degree in Information Systems, Computer Science, Finance, Business, or a related field; advanced degree or relevant certifications a plus.

Preferred Experience

• Hands-on experience with synthetic data generation and validation techniques.
• Experience with Oracle ERP or EPM environments and Finance data models (Chart of Accounts, master data).
• Experience supporting Finance, Accounting, or FP&A functions in a large, matrixed enterprise.
• Exposure to AI/ML data readiness, data products, or federated data governance models.