Drimva

Intuit

Principal, Finance Data Governance & Strategy

$215K–$291K

Mountain View, California +1 · Principal · Full-time · Posted Sep 15, 2026

Location
Mountain View, California
San Diego, California
Experience
8+ years in data governance, data strategy, or data architecture, including 2 to 3 years operating at a principal or strategic level.
Education
Bachelor's degree in Information Systems, Computer Science, Finance, Business, or a related field; advanced degree or relevant certifications a plus.

About the job

Owns Finance’s data governance strategy and technical foundation, including data stewardship, lineage, controls, synthetic data, and governance for platform modernization.

What you'll do

  • Define and operate Finance data governance standards, stewardship, ownership, quality, and access controls.
  • Build and maintain Finance data maps and lineage, and lead governance and reconciliation strategy for ERP and planning platform migration.
  • Set Finance’s synthetic data strategy and partner across teams on privacy-conscious data controls and enterprise data strategy.

What they ask for

  • 8+ years in data governance, data strategy, or data architecture, including 2 to 3 years 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 and owning enterprise data maps, catalogs, and lineage tooling.
  • Strong technical fluency, including SQL and modern data warehouse environments; experience in a regulated or controls-sensitive environment.

Nice to have

  • Hands-on experience with synthetic data generation and validation techniques.
  • Experience with Oracle ERP or EPM environments, Finance data models, or supporting Finance, Accounting, or FP&A in a large enterprise.

Pay

USD 215,000 - USD 291,000 base salary

Tools

SQL, Snowflake, BigQuery, Oracle ERP, EPM

AI focus

Lead Finance’s synthetic data strategy for testing, development, model training, and analytics; evaluate AI-driven data quality and support AI/ML data readiness.

Model-written summary · the original posting is authoritative

First seen Oct 28, 2026 · Last verified Nov 5, 2026 (UTC)