Data and AI Governance Lead
The Data and AI Governance Lead owns the governance framework for enterprise data and AI-enabled analytics across Azure, Fabric, and Purview. The role ensures data is classified, trusted, controlled, auditable, and safe for consumption by reporting, analytics, and AI tools – and that the platform is secure with continuously improving data quality. The postholder works with business data owners, IT security, architecture, engineering, and analytics teams to embed governance into ways of working, tooling, and technical delivery.
Key Accountabilities
- Own and mature the data and AI governance framework across Fabric, Purview, and the AI-enabled analytics platform; author and maintain policies and standards for data quality, metadata, lineage, retention, privacy, and ethical data and AI usage.
- Implement and own Purview catalogue, classification, lineage, glossary, data ownership, and certified dataset processes; drive master and reference data alignment of definitions, KPIs, and semantic standards across domains.
- Define and operate governance controls for AI-enabled data consumption, including the AI use case register covering risk rating, approval status, required controls, and review dates; establish the control checklist required before any AI use case goes live.
- Define rules for what data AI tools can and cannot access; ensure AI tools only consume approved, certified, classified, and traceable data with appropriate controls for prompt handling, output handling, personal data, sensitive data, and commercially sensitive data.
- Own audit evidence for AI-enabled data products including data source classification, ownership, access model, metric definition, lineage, and approval history.
- Implement data quality management including critical data elements, rule sets, monitoring, issue management, and remediation workflows; establish data quality standards, stewardship, and data owner accountability.
- Partner with cyber, legal, privacy, P&C, and business data owners to ensure AI use of enterprise data is safe, compliant, and auditable; partner with Cyber/InfoSec on data classification, access control, segregation of duties, and audit readiness.
- Support responsible AI practices including human oversight, explainability, traceability, and appropriate use of AI-generated outputs; support AI/ML governance including model risk controls, data suitability checks, and bias and ethical considerations.
Skills and Competencies
- Strong hands‑on expertise in Microsoft Purview: metadata management, classification, glossary, lineage, and certified dataset governance.
- Strong understanding of AI governance, responsible AI, AI risk assessment, and AI control frameworks; ability to classify AI use cases by risk and define appropriate pre‑go‑live controls.
- Ability to translate AI governance principles into practical technical controls implementable by engineers and architects, including prompt governance, output governance, data access controls, and audit evidence.
- Strong understanding of personal, sensitive, employee, and commercially sensitive data and access restrictions.
- Data quality framework design, data ownership, stewardship, and issue management; experience with profiling, rule design, monitoring, and root cause analysis.
- Knowledge of GDPR and PII controls with practical implementation experience in Azure.
- Strong stakeholder management across data, cyber, legal, privacy, P&C, and business domains; ability to challenge unsafe AI use cases and translate between technical and business audiences.
- Experience embedding governance into delivery pipelines including CI/CD, data contracts, and automated checks.
Qualifications and Experience
- Bachelor's degree in data analytics, data governance, data science, or a related discipline.
- Significant experience leading technical data governance in Microsoft Azure with a proven track record of improving data quality and stewardship using modern tooling.
- Extensive hands‑on experience with Microsoft Purview across an Azure data platform, with evidenced improvements to data quality and standardisation in a global context.
- Experience governing sensitive enterprise data including personal data, employee data, and business‑critical reporting data.
- Experience in AI governance, responsible AI, AI risk management, or AI data access controls; familiarity with frameworks such as NIST AI Risk Management, ISO AI Management standards, or equivalent.
- Practical implementation of international data standardisation frameworks for harmonising definitions, taxonomies, and formats across regions.
- Experience automating governance using modern tooling and embedding controls into data platform delivery.
- Relevant certifications such as DAMA/CDMP, DCAM, privacy, cloud, AI governance, or equivalent.
- Experience with AI/ML and data science platform use cases with comprehensive technical governance.
Equal Opportunity Employer
At RES we celebrate difference. We encourage applicants from diverse backgrounds, ideas, and points of view to build teams that solve complex problems. RES is an equal opportunity employer and we welcome candidates of all backgrounds.
