Key Accountabilities

• Design, implement, and lead the enterprise-wide data governance strategy, vision, and multi-year roadmap.
• Define and articulate a clear governance operating model that establishes how the business, IT, and data functions share accountability for enterprise data.
• Define key performance indicators (KPIs) and maturity metrics that measure program effectiveness, adoption, and business value.
• Support and contribute to the company's broader AI governance framework, specifically as it relates to data trust, lineage, classification, and fitness-for-use in AI and analytics workloads.

• Develop and roll out a practical business glossary, data classification schema, and enterprise data retention schedules.
• Stand up an enterprise data cataloging and metadata capability, including tool evaluation, selection, and initial implementation in partnership with IT and the Data Management function.
• Establish data quality standards, measurement practices, and remediation workflows for priority data domains.
• Develop the policies, procedures, controls, and training needed to operationalize governance across the enterprise.

• Partner with the CISO to align data classification with information security controls and incident response processes.
• Partner with the AI Leader to ensure data feeding AI and machine learning initiatives is governed, classified, and trusted.
• Partner with the Data Management Leader to ensure a clean division of responsibility between governance (policy, standards, stewardship) and data management (platforms, pipelines, operations).
• Establish an effective system of federated stewardship whereby business functions embrace data governance responsibilities and integrate controls into day-to-day operations.
• Educate and influence senior leaders on data-related risks, opportunities, and the business value of sustained governance.

Qualifications

• 6+ years of experience in data governance, data management, or closely related disciplines, with demonstrated progression into program leadership.
• Required: Proven track record of building a data governance program from the ground up, or leading a greenfield governance initiative inside a larger enterprise — including standing up the operating model, stewardship structure, and foundational artifacts (glossary, classification, catalog).
• Experience operating effectively as an individual contributor at the director level, delivering outcomes through influence across peer functions rather than direct reporting authority.
• Hands-on experience evaluating, selecting, and implementing a modern data catalog and governance platform (e.g., Collibra, Alation, Atlan, Informatica, Microsoft Purview).
• Industry background in manufacturing, industrial, aerospace/defense, medical device, or other regulated or IP-sensitive environments strongly preferred.

• Bachelor's degree required. Advanced degrees and credentials demonstrating strong analytical and research skills are valued.
• Preferred certifications: CDMP (Certified Data Management Professional) — Practitioner or Master level.
• CGEIT (Certified in the Governance of Enterprise IT).
• DCAM (Data Management Capability Assessment Model) familiarity.
• AIGP (AI Governance Professional) or ISO/IEC 42001 knowledge is a plus given the AI governance intersection.

• Strong understanding of data governance and data management principles, frameworks (DAMA-DMBOK), and maturity models.
• Ability to translate complex technical concepts into clear business terms for executive audiences.
• Strong influencing, facilitation, and communication skills; experience building alliances across IT, Security, Legal, and Business functions.
• Pragmatic, roadmap-driven mindset — able to sequence a greenfield build so early wins fund long-term program maturity.
• Comfort operating in ambiguity and defining structure where none exists today.

First-Year Outcomes

• A published enterprise data governance strategy, operating model, and multi-year roadmap endorsed by executive leadership.
• A working data classification schema and initial business glossary covering priority domains.
• A selected and implemented data catalog platform with initial population across high-value data domains.
• An established federated stewardship model with identified business data owners and stewards.
• A defined set of KPIs and a baseline maturity assessment to measure program progress going forward.