Data and AI Governance Technical Lead

Location: Kings Langley

Description Data and AI Governance Technical Lead Do you want to Make Power for GoodRES is the world's largest independent renewable energy company. Our mission is simple: a future where everyone has access to affordable, zero-carbon energy. The problems we're solving are among the most important of our generation — and the people working on them are extraordinary.

As AI adoption accelerates across the business, the integrity and trustworthiness of RES's data has never mattered more. This is a rare opportunity to own that — technically, practically, and at global scale.

The Role As Data and AI Governance Technical Lead, you'll own the technical governance framework for enterprise data and analytics across Azure, Fabric, and Purview. You'll ensure data is classified, trusted, controlled, auditable, and safe for consumption by reporting, analytics, and AI tools in our global, centralised Azure Fabric data platform — and that governance is embedded into how the platform is built and operated.

This is a hands-on technical role. You'll implement controls, own Purview, define AI use case governance, and work closely with engineering, architecture, cyber, legal, and business teams to make governance real. You'll be the person who turns AI governance principles into practical, implementable technical controls.

You will also lead the delivery of a data governance framework for the globe, including data ownership, stewardship and support literacy and culture work, including chairing regular forums with senior executives. What You'll DoOwn and continuously mature the data governance framework across Fabric, Purview, and the AI-enabled IT Azure analytics platform.

Support IT to author and maintain policies and standards for data quality, metadata, lineage, retention, privacy, and ethical data and AI usage.

Support master and reference data alignment — developing definitions, KPIs, and semantic standards across global domains.

Implement international data standardisation frameworks to ensure consistent definitions, taxonomies, and formats across regions.

Implement and own Purview catalogue, classification, lineage, glossary, data ownership, and certified dataset processes end-to-end.

Ensure all enterprise data assets are classified, owned, documented, and auditable.

Embed governance metadata and lineage into data platform delivery as a standard engineering practice.

Define and operate governance controls for AI-enabled data consumption through the central Azure data platform within IT.Define and enforce rules for what data and AI tools can and cannot access through the IT Azure data platform; ensure AI tools consume only approved, certified, and traceable data.

Own audit evidence for AI-enabled data products in the Azure platform — maintaining complete, defensible records of data lineage, classification, approval, and access history.

Support IT in enabling responsible AI practices including human oversight, explainability, traceability, bias assessment, and ethical use.

Implement data quality management across the Azure platform — defining critical data elements, rule sets, monitoring, issue management, and remediation workflows.

Establish data quality stewardship and data owner accountability across business domains.

Automate data quality checks and embed them into CI/CD pipelines and data platform delivery processes.

Partner with cyber and IT on data classification, access control, segregation of duties, and audit readiness.

Work with legal, privacy, P&C, and business data owners to help ensure data use of enterprise data is safe, compliant, and auditable.

Translate governance requirements into practical technical specifications for IT data engineers and data architects — and hold delivery teams accountable.

What You'll Bring Microsoft Purview — deep hands-on expertise across metadata management, classification, glossary, lineage, and certified dataset governance in an Azure environment.

AI governance — strong understanding of responsible AI, AI risk assessment, and AI control frameworks; ability to classify AI use cases by risk and define pre-go-live controls.

Data governance strategy, frameworks, ownerships and…