Build and lead a scalable data governance program and operating model ensuring data is accurate, consistent, and secure across the enterprise
Define governance operating rhythms and decision frameworks (forums, ownership, escalation paths) that scale with the business
Drive adoption of governance practices through enablement, communication, and clear expectations—treating governance as a product
Define and implement data governance policies aligned with privacy, security, compliance, and ethical AI standards
Partner with Security, Legal, and Engineering to embed governance into tooling and workflows, enabling safe innovation in AI and analytics
Lead the strategy and implementation of a semantic/context layer (business glossary, metrics, data contracts, lineage) to standardize how data is understood
Ensure consistent data definitions and integration into core tools (BI, catalogs, transformation workflows) to reduce metric drift and improve usability
Improve data quality and trust at scale, including defining KPIs and driving remediation with data owners and engineering teams
Establish golden data sources, standards, and stewardship models to ensure accountability and cross-functional ownership
Develop a governance framework that supports privacy, security, compliance, and business continuity, including audit readiness, risk management, and access controls

5+ years in data governance, data management, analytics engineering, data program management, or adjacent roles with strong cross-functional influence.
Demonstrated ability to build governance programs that people actually use—balancing enablement with control.
Strong understanding of privacy/security principles as they apply to data (access controls, classification, retention, least privilege, auditability).
Experience defining and operationalizing data quality programs and working with engineering/data teams on remediation.
Proven success partnering with leaders across Product, Engineering, Analytics, Security, Legal/Privacy, and Operations.
Excellent communication skills—able to translate between technical teams and business stakeholders.
Healthcare / regulated data experience (e.g., HIPAA/PHI environments, sensitive member/patient data, clinical or outcomes-oriented analytics) (preferred).
Experience enabling ethical and responsible AI practices (e.g., governance for training data, model inputs/outputs, risk reviews, bias considerations, human-in-the-loop controls) (preferred).
Hands-on experience implementing or operating a semantic layer / metrics layer / business glossary and building shared definitions across BI and data products (preferred).
Familiarity with modern data governance and analytics tooling (examples: data catalogs, lineage/metadata, metric layers, data quality monitoring, and transformation frameworks—tool-agnostic experience is fine) (preferred).

Data Governance Manager

Key skills