Key responsibilities
Lead data governance strategy & operating model
- Define and drive data governance principles, policies, standards, frameworks and controls (including data quality expectations).
- Align governance to business goals and enable a “data-driven culture.”
Establish and oversee data stewardship & portfolio governance
- Provide leadership on governance setup across domains (e.g., ownership, accountability, coordination).
- Embed and Oversee governance processes that ensure consistent decision-making and escalation.
Ensure compliance, privacy, and security across the data & AI lifecycle
- Ensure data is sourced/handled/developed/deployed legally and ethically.
- Support privacy, security, and compliance requirements through governance and enforcement mechanisms.
Own enterprise Responsible AI governance (risk, controls, auditability)
- Govern AI risk, compliance, privacy, and security using formal oversight.
- Ensure documentation, validation/controls, training, and continuous monitoring to keep AI systems safe, compliant, and audit-ready.
Lead scalable solution delivery and data/AI architecture governance
- Influence architecture and delivery approaches so governance is embedded into platform and solution lifecycles.
- Ensure governance is practical and repeatable across programs.
- Enforce data governance practices into Business As Usual activities.
Lead a data culture within different organizations
- Develop, curate, and deliver Data Governance training for data stewards, data custodians, and the broader data community.
- Develop reusable assets that strengthen and support the data governance capability.
Lead the development of RFP responses
- Establish the response structure and define the proposed solution approach for each RFP.
- Coordinate and consolidate contributions from all relevant solution teams.
- Mobilise the appropriate subject-matter experts and delivery teams to support Data Governance RFP responses.
Mentor and develop teams
- Coach specialists and leads; build capability through standards, guidance, and knowledge sharing.
Required skills & competencies
- Strong experience in enterprise data governance (strategy, standards, stewardship, data quality, policy enforcement).
- Proven ability to apply Responsible AI governance concepts (risk/control frameworks, documentation, monitoring).
- Deep understanding of compliance, privacy, and security considerations for data and AI deployments.
- Leadership capability: managing governance at portfolio/program level and influencing stakeholders.
- Ability to translate governance requirements into scalable delivery practices.
- Leading cross functional multinational teams
Typical background (examples—may vary by project)
- Leadership experience in data governance, data management, AI/ML governance, or enterprise risk/compliance for data & AI.
- Cross-functional experience with legal/privacy/security and delivery teams.
Qualification
- Data Science
- Computer Science
- Information Systems
- Information Management
- AI / Machine Learning
- Cybersecurity
- Law / Compliance / Risk Management (if paired with strong data & AI governance experience)
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