Data Governance Lead

Job Title Data Governance Lead Job Description Summary The Data Governance Lead will establish and operate practical governance across the DNA Data and Analytics portfolio. The role ensures that priority data and analytics products have clear ownership, agreed definitions, measurable quality, traceable lineage, appropriate controls, and evidence of fitness for purpose. Working with business owners, stewards, technology teams, analytics squads, risk functions, and supply partners, the lead will translate enterprise requirements into proportionate practices that support trusted decision-making, regulatory compliance, and AI readiness. Business and process owners remain accountable for source-data accuracy and remediation; the Data and Analytics team provides the governance method, semantic context, quality evidence, and trusted consumption layer.

Key accountabilities 1. Governance strategy, operating model, ownership, and stewardship Define and operate the DNA governance roadmap, standards, decision rights, and proportionate controls, aligned with enterprise policy and data risk. Establish clear governance roles, decision rights, and escalation paths across Data Owners, Data Stewards, Product Owners, technology teams, analytics teams, and supply partners. Ensure governance is proportionate to business value, data criticality, regulatory exposure, and delivery risk. Maintain a prioritised governance plan with clear milestones, dependencies, decision points, and measures of adoption. Review the plan regularly with DNA leadership and adjust it as business priorities, regulatory expectations, architecture, delivery capacity, and data risks change. Establish clear Data Owner and Data Steward coverage for priority domains, including responsibilities, approval rights, escalation paths, coaching, and resolution of ownership gaps. Enable owners and stewards to fulfil their responsibilities through practical playbooks, role-based guidance, decision templates, and regular coaching. Monitor coverage and participation, identify persistent gaps, and escalate where unclear accountability prevents timely approval, issue resolution, or responsible use of data.   2. Semantics, metadata, critical data, and data quality Maintain agreed business definitions, semantic standards, metadata, data dictionaries, and catalogue records so that priority data products are discoverable, understandable, traceable, and reusable. Lead the identification of Critical Data Elements and the lifecycle of business and data-quality rules, including agreed dimensions, thresholds, monitoring, root-cause analysis, source remediation, exception management, and escalation. Partner with domain experts, data engineers, analysts, and control functions to ensure definitions and quality rules are implementable and consistently interpreted. Use profiling and monitoring results to identify material issues, agree remediation ownership and target dates, document accepted exceptions, and report trends that require management attention.   3. Data-quality monitoring, lineage, controls, and governed data products Oversee adoption of data-quality monitoring and trust indicators, ensuring that rules are linked to approved definitions, accountable owners, agreed thresholds, and remediation actions. Ensure that material data flows, transformations, dependencies, controls, approvals, exceptions, and lifecycle decisions are traceable and supported by appropriate evidence. Embed proportionate governance checkpoints into the DNA product lifecycle and confirm that material products have accountable owners, approved sources and definitions, quality controls, usage guidance, and certification evidence. Work with product and engineering teams from intake through release and ongoing operation so governance requirements are designed in rather than added late. Apply risk-based review criteria, document conditions or exceptions, and ensure certification can be revisited when sources,