The Manager: AI Data Governance & Data Management is responsible for leading and operationalizing an enterprise-wide AI data governance and data management framework that ensures organizational data is trusted, secure, accessible, and managed as a strategic business asset.

The role establishes governance policies, standards, controls, and best practices that improve data quality, consistency, privacy, and usability while enabling responsible Artificial Intelligence (AI), regulatory compliance, and data-driven decision-making.

Working closely with Data Platform Engineering, Data Science, AI/ML teams, Information Security, Risk, Compliance, Legal, and business stakeholders, the role embeds governance throughout the data lifecycle and supports the responsible use and value realization of enterprise data assets.

Key Performance Areas (KPAs)

1. Data Governance Strategy & Framework

• Develop, implement, and continuously improve the enterprise AI data governance framework, operating model, policies, and standards.
• Establish governance principles covering:
  • Data ownership
  • Data stewardship
  • Decision rights
  • Responsible AI
  • Ethical data usage
• Promote standardized governance practices across the organization to improve consistency, reduce duplication, and support enterprise-wide data management.

2. Operational Delivery

2.1 Data Governance & Stewardship

• Establish and facilitate enterprise data governance forums with clearly defined roles, responsibilities, and decision-making processes.
• Implement and operationalize data ownership and stewardship across business and technology domains.
• Maintain enterprise data policies, standards, business glossaries, and governance documentation aligned with recognized industry frameworks such as DAMA-DMBOK and DCAM.

2.2 Data Management & Quality

• Define enterprise data quality standards, metrics, and remediation processes for critical business data.
• Lead Master Data Management (MDM), reference data management, and metadata management initiatives.
• Ensure enterprise data is:
  • Accurate
  • Consistent
  • Discoverable
  • Documented
  • Traceable throughout its lifecycle
• Partner with Data Platform Engineering teams to embed governance controls into data pipelines, warehouses, analytics platforms, and reporting solutions.

2.3 AI & Model Governance (Responsible AI)

• Establish governance controls for data used in AI and Machine Learning, including:
  • Training data lineage
  • Data quality
  • Fitness for purpose
  • Bias monitoring
  • Representativeness
• Define and enforce Responsible AI principles, including:
  • Fairness
  • Transparency
  • Explainability
  • Accountability
• Implement model governance practices covering:
  • Model inventory
  • Model risk management
  • Ongoing monitoring
• Monitor emerging AI regulations and translate regulatory requirements into practical governance controls.

2.4 Privacy, Security & Regulatory Compliance

• Embed Privacy-by-Design principles into enterprise data management practices.
• Define and enforce standards for:
  • Data classification
  • Access management
  • Data retention
  • Data minimization
  • Information protection
• Ensure compliance with applicable privacy legislation, industry regulations, and organizational security policies.
• Support regulatory reporting, audits, compliance assessments, and governance reviews through well-managed and traceable data assets.

2.5 Data Literacy & Value Enablement

• Promote a data-driven culture through awareness, education, training, and communities of practice.
• Support responsible use and value realization of enterprise data assets while maintaining privacy, consent, and ethical standards.
• Establish governance controls for internal and external data sharing.

3. Governance & Risk Management

Governance Forums

• Participate in enterprise Data and AI Governance Committees.
• Provide governance leadership and guidance across strategic data initiatives.
• Facilitate collaboration between Data Engineering, Data Science, Information Security, Risk, Compliance, Product, and Business teams.

Escalation Management

Resolve governance issues relating to:

• Data quality and integrity
• Data privacy and regulatory compliance
• AI and model risk
• Data ownership and stewardship
• Business impact and delivery risks

Job Requirements

Education

• Bachelor’s Degree in:
  • Computer Science
  • Data Science
  • Information Systems
  • Information Technology
  • Engineering
  • Or a related discipline
• Master’s Degree or MBA is advantageous.

Professional Certifications (Preferred)

• Certified Data Management Professional (CDMP)
• Data Governance Certification (e.g., DCAM)
• Cloud Certifications (Azure, AWS, Google Cloud)
• Project or Change Management Certifications (PMP, Agile, Lean Six Sigma)

Experience

• Minimum 5–8 years’ experience in:
  • Data Governance
  • Data Management
  • Enterprise Data Management
• Demonstrated experience implementing enterprise data governance frameworks within large and highly regulated organizations.
• Strong understanding of:
  • Data platforms
  • Business Intelligence
  • Analytics
  • Artificial Intelligence
  • Machine Learning governance
• Proven experience leading cross-functional enterprise data initiatives.
• Experience working across geographically distributed organizations or complex enterprise environments is advantageous.
• Strong stakeholder management skills with the ability to communicate complex technical concepts to executive and non-technical audiences.

Technical Competencies

Data Governance & Metadata Management

• Data Governance Frameworks
• Master Data Management (MDM)
• Metadata Management
• Business Glossaries
• Data Catalogues
• Data Lineage

Data Governance Platforms

Experience with tools such as:

• Collibra
• Informatica
• Talend
• Alation
• Microsoft Purview
• Similar enterprise governance platforms

Data Platforms

• SQL
• Hadoop
• Apache Spark
• Snowflake
• BigQuery
• Azure Synapse
• Cloud-native data platforms