Director, Data Governance

Data And AI Governance Leader

Responsible for defining, operationalizing, and scaling the enterprise governance framework for data assets, AI/ML models, and AI agents. This leader ensures that enterprise data is trusted, protected, and well-managed across its lifecycle, and that models and agents are governed with appropriate standards for transparency, accountability, security, compliance, and business value realization. This role serves as the control point between business enablement and risk management - partnering across Data, Engineering, Security, Legal, Risk, Compliance, Architecture, and business stakeholders to create a governance operating model that enables innovation without compromising trust, regulatory readiness, or operational discipline.

Responsibilities:

• Defines and leads the enterprise Data & AI Governance strategy, operating model, policies, standards, and decision-rights framework across data, AI/ML models, and AI agents.
• Establishes a scalable governance structure spanning business governance (policy, stewardship, ownership, traceability) and technical governance (quality, lineage, observability, monitoring, controls).
• Translates regulatory, security, privacy, and enterprise risk requirements into practical governance controls, workflows, and approval processes.
• Chairs governance forums and escalation bodies that review material risks, policy exceptions, control gaps, and remediation priorities.
• Owns enterprise data governance policies and standards for data classification, sensitivity labeling, masking/tokenization, restricted data access management, usage controls, and encryption alignment across the data lifecycle.
• Defines and enforces standards for metadata management, business glossary, semantic definitions, lineage, stewardship, technical metadata harvesting, and data catalog governance.
• Leads enterprise data quality governance, including critical data element identification, data quality rules management, issue management, root-cause remediation, certification, and quality scorecards. Implements data certification and "trusted data product" framework.
• Governs data lifecycle management, including retention, archival, legal hold alignment, secure destruction, and disposal of structured and unstructured data assets in accordance with policy and regulatory requirements.
• Establishes standards for trusted data products and ensures data consumed by analytics, AI, and agents is authoritative, traceable, and fit for purpose.
• Defines governance across the AI/ML lifecycle, such as model development standards, validation, explainability, and bias mitigation, monitoring, drift detection, and retraining.
• Ensures compliance with emerging AI regulations, responsible and ethical AI frameworks, and regulatory requirements (e.g., CCPA, GDPR, model risk guidelines) by establishing governance standards, controls, and accountability practices for AI transparency, data privacy, sensitive data handling, and protection.
• Establishes and maintains AI/ML model governance, including model inventory, documentation, auditability, lifecycle controls, validation, monitoring, and oversight.
• Partners with Legal, Risk, Security, CDO, and business leaders to embed data and AI governance into enterprise workflows, ensuring governance enables—not slows—business adoption of data and AI capabilities.
• Ensures governance is embedded and automated into data pipelines, BI/semantic layers, and AI product development processes to support scalable, consistent, and proactive controls.
• Drives organization-wide data literacy, governance awareness, and adoption of governance practices.
• Leads governance councils and cross-functional forums to review governance priorities, risks, decisions, and remediation activities.
• Provides executive reporting on governance maturity, risks, compliance, and key performance indicators.
• Leads, mentors, and develops a high-performing finance team, promoting a culture of accountability, continuous learning, and performance excellence.
• Fosters collaboration and a positive team environment by promoting cohesion, professional growth, and shared success.
• Performs other duties and projects assigned.

Requirements:

• Bachelor's degree in Information Technology or similar field and minimum of ten (10) + years of experience in data governance, data management, AI governance, model risk management, information governance, or related disciplines.
• Minimum of five (5) + years in a senior leadership role overseeing enterprise governance programs.
• Strong executive presence and cross-functional influence.
• Experience driving enterprise-wide transformation and behavior change.
• Proven experience building and running a centralized Data & AI Governance team or center of excellence, including governance councils, stewardship models, enterprise policy frameworks, and cross-functional operating rhythms.
• Deep expertise in enterprise data governance domains, including data classification, metadata, lineage, data quality, access management, masking,…