Data & AI Governance Lead – S4+
On-siteGlasgow, Scotland, United Kingdom or Fort Worth, Texas, United States
Job Summary
Own delivery of enterprise-grade data and AI governance across the S4+ programme, establishing governance-by-design expectations for the delivery lifecycle. Embed ownership, stewardship, data quality, privacy, security, and Responsible AI controls by design to ensure solutions are safe, trusted, and scalable. Partner with the Transformation Data & AI Architect to ensure architectural decisions are supported by appropriate governance controls and reusable standards. Track, report, and escalate data and AI risks early, proposing mitigations and supporting evidence-based decisions for programme leaders. Define BAU readiness criteria and ensure governance artefacts are transition-ready for post-go-live adoption.
Required Qualifications
- Knowledge of data governance, data management and Responsible AI practices
- Knowledge of privacy, security, regulatory and AI risk obligations
- Knowledge of characteristics of trusted, AI ready data
- Skill in translation of policy into executable controls
- Skill in facilitation, escalation and risk articulation
- Skill in collaboration with architecture, delivery, legal, security and risk
- Capability to embed governance into delivery without slowing progress
- Capability to operate as first line governance accountability
- Capability to ensure governance artefacts are BAU ready post go live
- Experience delivering data governance and/or AI governance in large programmes
- Experience embedding ownership, stewardship, data quality and Responsible AI by design
- Experience operating with board safe judgement while remaining outcome focused
- Willingness to work in close partnership with the Transformation Data & AI Architect
- Willingness to track, report, and escalate data & AI risk and compliance
- Willingness to ensure governance requirements are designed into architecture patterns and reusable standards
- Willingness to define and embed data quality controls (rules, thresholds, monitoring expectations, defect management and remediation governance)
- Willingness to embed privacy, security, and regulatory controls aligned to enterprise obligations
- Willingness to embed Responsible AI controls appropriate to AI-enabled capabilities (traceability, transparency, auditability, appropriate use, risk classification, oversight)
- Willingness to define BAU readiness criteria for governance
- Willingness to define early-life support routines for governance issues post go-live
- Willingness to ensure solutions are safe, trusted, and scalable by embedding controls into delivery and operating routines
- Willingness to embed ownership, stewardship, data quality, privacy, security, and Responsible AI controls by design
- Willingness to establish governance-by-design expectations across the delivery lifecycle (design, build, test, migration, cutover, stabilisation)
- Willingness to provide a single accountable owner for governance delivery across S4+
- Willingness to ensure consistent standards, controls, and adoption readiness
- Willingness to ensure governance artefacts and controls are transition-ready for BAU adoption post go-live
- Willingness to identify risks early, propose mitigations, and ensure clear action ownership
- Willingness to support evidence-based decision packs for programme leaders and Exec/Board governance where material risk decisions are required
- Willingness to ensure governance artefacts and controls are BAU-ready post go-live
- Willingness to define early-life support routines for governance issues post go-live (triage, escalation, remediation, monitoring) until BAU stabilises
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