Job Title
Data Governance LeadJob 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.Job Description
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, transformations, controls, intended use, or regulatory obligations materially change.
4. Stakeholder leadership, risk, assurance, and change
Lead governance forums and cross-functional engagement to resolve ownership, definitions, quality, remediation, product-readiness, risk, and exception decisions, with clear actions and escalation paths.
Prepare concise decision papers and governance reporting for senior stakeholders, presenting the business impact, control implications, options, dependencies, and recommended action. Track commitments to closure and intervene when cross-domain conflicts, delivery constraints, or unresolved ownership place trusted outcomes at risk.
Support data-risk assessments, control reviews, audits, regulatory enquiries, and remediation by maintaining clear evidence, issue ownership, exceptions, and management reporting.
Build governance capability through practical guidance, training, coaching, and targeted automation of workflows, metadata capture, quality monitoring, evidence, and reporting.
Promote adoption by integrating governance into existing delivery routines, product ceremonies, and operational processes. Identify opportunities to simplify controls, reduce manual evidence gathering, and improve user experience while maintaining appropriate accountability, transparency, and assurance.
Key deliverables
Core deliverables include the governance roadmap and operating model; ownership and stewardship records; approved definitions, metadata, Critical Data Elements, and quality rules; lineage and control evidence; product-certification records; issue and remediation reporting; and stewardship enablement materials.
Experience and qualifications
Essential:
Bachelor's degree or equivalent experience in Data Management, Information Management, Computer Science, Information Systems, Business, Risk, or a related discipline.
At least eight years of relevant experience in data governance, enterprise data management, data quality, metadata, information architecture, data risk, or a related field.
Demonstrable experience establishing or operationalising a data governance framework in a complex, matrixed organisation.
Strong working knowledge of data ownership and stewardship, metadata and business semantics, Critical Data Elements, data quality, lineage, lifecycle controls, and governance of data and analytics products.
Experience facilitating governance councils, working groups, workshops, and cross-functional decision forums.
Ability to translate policies and technical concepts into practical business processes.
Strong stakeholder-management and influencing skills, including the ability to challenge constructively and obtain decisions from senior stakeholders.
Practical understanding of data platforms, data warehouses or lakes, ETL or data pipelines, BI environments, and system integrations.
Desirable:
Experience in banking, financial services, corporate real estate, workplace services, facilities, asset management, or another regulated environment.
Familiarity with DAMA-DMBOK, DCAM, EDM Council practices, or comparable data-management frameworks.
Experience with enterprise data-governance, catalogue, metadata, data-quality, or lineage platforms.
Working knowledge of SQL and the ability to interpret data models, profiling outputs, lineage, and transformation logic.
Familiarity with modern analytics, business-intelligence, distributed-data, or cloud data platforms.
Leadership profile and success measures
The successful candidate is pragmatic, outcome focused, influential, and technically credible. They create clarity across complex ownership and data flows, challenge weak accountability constructively, and build adoption through collaboration, coaching, and visible business value.
Success will be measured through ownership coverage for priority domains; completeness of approved definitions, metadata, lineage, and quality controls; adoption of governance checkpoints; timely source remediation of material issues; product-certification status; closure of risk and audit actions; and stakeholder confidence in DNA-supported products.
The lead will establish a practical baseline and targets for these measures, provide transparent reporting on progress and material exceptions, and use the evidence to prioritise improvement. Success is demonstrated not only by completed governance artefacts, but by faster decisions, clearer accountability, reduced recurrence of material data issues, stronger audit readiness, and increased reuse of trusted data products.
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