> Markdown version of [/jobs/ext/2264353-data-ai-governance-lead-s4](https://www.wearedevelopers.com/jobs/ext/2264353-data-ai-governance-lead-s4). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data & AI Governance Lead - S4+ - **Company:** Weir Minerals - **Location:** Glasgow, UK - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Software Bug Management, EXEC (Scripting Language), Encodings, Data Governance, Data Management - **Published:** August 27, 2026 - **Apply:** https://weir.wd3.myworkdayjobs.com/Weir_External_Careers/job/Glasgow/Data---AI-Governance-Lead---S4-_R0038150 ## About the Role * Data governance, data management and Responsible AI practices. * Privacy, security, regulatory and AI risk obligations. * Characteristics of trusted, AI ready data. Skills * Translation of policy into executable controls. * Facilitation, escalation and risk articulation. * Collaboration with architecture, delivery, legal, security and risk. Capabilities * Embed governance into delivery without slowing progress. * Operate as first line governance accountability. * Ensure governance artefacts are BAU ready post go live. Experience * Delivering data governance and/or AI governance in large programmes. * Embedding ownership, stewardship, data quality and Responsible AI by design. * Operating with board safe judgement while remaining outcome focused. ## Description Designs, embeds, and operates enterprise grade data and AI governance across the S4+ programme, ensuring data, analytics, and AI assets are trusted, compliant, and ready for scale. The role works into the Data Workstream and is responsible for delivery, ensuring that data, analytics and AI assets created or impacted by S4+ are trusted, compliant, well governed and ready for scale., Own delivery of Data & AI governance for S4+ Objective: Provide a single accountable owner for governance delivery across S4+, ensuring consistent standards, controls, and adoption readiness. * Own delivery of enterprise grade Data & AI governance across the programme. * Establish governance-by-design expectations across the delivery lifecycle (design, build, test, migration, cutover, stabilisation) so controls are embedded early, not retrofitted. Embed ownership, stewardship, data quality, privacy, security, and Responsible AI controls by design Objective: Ensure solutions are safe, trusted, and scalable by embedding controls into delivery and operating routines. * Embed data ownership and stewardship roles, responsibilities, and decision cadence across domains and workstreams. * Define and embed data quality controls (rules, thresholds, monitoring expectations, defect management and remediation governance). * Embed privacy, security, and regulatory controls aligned to enterprise obligations; ensure compliant handling of sensitive/regulated data in pipelines and analytics. * Embed Responsible AI controls appropriate to AI-enabled capabilities (traceability, transparency, auditability, appropriate use, risk classification, oversight). Work in close partnership with the Transformation Data & AI Architect Objective: Ensure architectural decisions are supported by appropriate governance controls. * Work in close partnership with the Transformation Data & AI Architect. * Ensure architectural decisions are supported by governance controls (ownership, stewardship, data quality, privacy/security, Responsible AI). * Ensure governance requirements are designed into architecture patterns and reusable standards. Track, report, and escalate data & AI risk and compliance Objective: Make risk visible early, manage it actively, and ensure timely escalation where delivery or enterprise obligations are threatened. * Track, report, and escalate data & AI risk and compliance. * Operate as first line governance accountability: identify risks early, propose mitigations, and ensure clear action ownership. * Support evidence-based decision packs for programme leaders and Exec/Board governance where material risk decisions are required. Ensure governance artefacts and controls are BAU-ready post go-live Objective: Ensure BAU inherits a working governance operating model, not just documentation. * Ensure governance artefacts and controls are transition-ready for BAU adoption post go-live. * Define BAU readiness criteria for governance (ownership assigned, monitoring live, exception handling operating, forums scheduled) and ensure criteria are met ahead of final programme gates. * Define early-life support routines for governance issues post go-live (triage, escalation, remediation, monitoring) until BAU stabilises. ## Related Videos - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) - [Responsible AI @ Microsoft - Governance, Standards, Learnings](https://www.wearedevelopers.com/videos/1544-responsible-ai-microsoft-governance-standards-learnings) - [A Brief History of Data Storage](https://www.wearedevelopers.com/videos/974-a-brief-history-of-data-storage) - [It's all about the Data](https://www.wearedevelopers.com/videos/425-it-s-all-about-the-data) - [Big Business, Big Barriers? Stress-Testing AI Initiatives.](https://www.wearedevelopers.com/videos/1022-big-business-big-barriers-stress-testing-ai-initiatives) - [AI beyond the code: Master your organisational AI implementation.](https://www.wearedevelopers.com/videos/1248-ai-beyond-the-code-master-your-organisational-ai-implementation) ## Related Articles - [Panel Discussion: Responsible AI in Practice - Real-World Examples and Challenges](https://www.wearedevelopers.com/magazine/488-panel-discussion-responsible-ai-in-practice-real-world-examples-and-challenges) - [Trustworthy AI Starts at Deployment: 5 Checks Before You Ship](https://www.wearedevelopers.com/magazine/753-trustworthy-ai-starts-at-deployment-5-checks-before-you-ship) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere) - [Should AI be Regulated? The Arguments For and Against](https://www.wearedevelopers.com/magazine/271-should-ai-be-regulated-the-arguments-for-and-against)