Description

Data and AI Governance Technical Lead

Make Power for Good

RES 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 AI across Azure, Fabric, and Purview. You'll ensure data is classified, trusted, controlled, auditable, and safe for consumption by reporting, analytics, and AI tools — 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.

At a time when the EU AI Act is reaching full enforcement and AI risk is a board-level concern, this role sits at the centre of how RES manages that responsibly.

What You'll Do

Governance Framework & Standards

• Own and continuously 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.
• Drive master and reference data alignment — harmonising definitions, KPIs, and semantic standards across global domains.
• Implement international data standardisation frameworks to ensure consistent definitions, taxonomies, and formats across regions.

Microsoft Purview & Data Catalogue

• 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.

AI Governance & Control

• 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 technical control checklist required before any AI use case goes live: data source classification, ownership, access model, metric definition, lineage, prompt handling, output handling, and personal and sensitive data controls.
• Define and enforce rules for what data AI tools can and cannot access; ensure AI tools consume only approved, certified, and traceable data.
• Own audit evidence for AI-enabled data products — maintaining complete, defensible records of data lineage, classification, approval, and access history.
• Support responsible AI practices including human oversight, explainability, traceability, bias assessment, and ethical use; align to frameworks such as NIST AI RMF, ISO 42001, or the EU AI Act as applicable.

Data Quality

• Implement data quality management across the 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.

Cross-functional Partnership

• Partner with cyber and InfoSec on data classification, access control, segregation of duties, and audit readiness.
• Work with legal, privacy, P&C, and business data owners to ensure AI use of enterprise data is safe, compliant, and auditable.
• Translate governance requirements into practical technical specifications for engineers and architects — and hold delivery teams accountable.
• Challenge unsafe AI use cases; communicate risk to both technical and non-technical stakeholders.

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.
• Technical control design — able to translate governance principles into implementable controls: prompt governance, output governance, data access restrictions, and audit evidence.
• Data quality — experience designing and operating data quality frameworks including profiling, rule design, monitoring, and root cause analysis.
• Privacy & compliance — working knowledge of GDPR, PII controls, and sensitive data handling with practical Azure implementation experience.
• Governance automation — experience embedding governance into CI/CD pipelines, data contracts, and automated platform checks.
• Stakeholder influence — comfortable challenging unsafe practices and translating between technical teams, cyber, legal, privacy, and business.
• AI regulatory landscape — familiarity with NIST AI RMF, ISO 42001, EU AI Act, or equivalent frameworks.

Your Background

Essential

• Degree in data governance, data science, computer science, or a related field — or equivalent hands-on experience.
• Significant experience leading technical data governance in Microsoft Azure, with a proven track record of improving data quality, stewardship, and auditability using modern tooling.
• Extensive hands-on Microsoft Purview experience across an Azure data platform, with evidenced improvements to data quality and standardisation at global scale.
• Experience designing and implementing AI governance controls — including AI use case risk assessment, data access controls for AI tools, and audit evidence management.
• Experience governing sensitive enterprise data including personal data, employee data, and business-critical reporting data.
• Practical experience embedding governance into data platform delivery — CI/CD, automated checks, data contracts.
• Familiarity with AI governance frameworks such as NIST AI RMF, ISO 42001, or EU AI Act.

Desirable

• Experience with automation tooling such as Power Platform, Power Automate, or equivalent applied to governance workflows.
• Python or SQL used in a data quality or governance automation context.
• Relevant certifications: DAMA/CDMP, DCAM, CIPP, AIGP, Microsoft Azure, or equivalent AI or data governance credentials.

Why RES?

• Own something that matters — as AI becomes central to how RES operates, data governance is no longer a back-office function; it's a business-critical capability.
• A modern Azure/Fabric/Purview stack with real complexity, global scale, and active investment.
• A collaborative data function where governance is built in from the start — not retrofitted.
• Competitive salary, benefits, and commitment to your professional development.

At RES, we celebrate difference. We encourage applicants with different backgrounds, ideas, and points of view — our multiple perspectives make us better at solving complex problems. We welcome applications regardless of ethnicity, culture, gender, nationality, age, sexual orientation, gender identity, disability, marital status, parental status, or social background.