Data Governance Lead

Purpose

We are seeking a Data Governance Lead to design and ope rationalise our enterprise data governance capability. This is a high‑impact role for a practitioner who can translate strategy into a working governance operating model — defining domains, curating an enterprise data catalog, standing up business glossaries, codifying critical data elements, and embedding measurable OKRs that demonstrate governance value to the business.

You will own the governance blueprint and drive adoption across data domains, partnering with stewards, owners, engineering, security, privacy, and risk to make trusted, well‑governed data the default—not the exception. The role is platform‑agnostic; what matters is your ability to design and run an effective governance capability regardless of the underlying tooling.

• Define and evolve the enterprise data governance strategy, target operating model, and multi‑year roadmap aligned to business and regulatory priorities.
• Establish the federated governance model, balancing central standards with domain‑level accountability, in line with data product / data mesh principles where appropriate.
• Author and maintain the governance policy stack: data governance policy, data quality standard, metadata standard, ownership and stewardship policy, and supporting procedures.
• Define the governance forums and decision rights, including the Data Governance Council, domain working groups, and escalation paths.

• Define the governance domain model (e.g., Customer, Finance, Product, Risk, HR), assigning domain owners, data owners, stewards, and custodians with clearly documented RACI.
• Build and run the steward enablement programme: onboarding, playbooks, training, office hours, and certification paths.
• Establish performance expectations and maturity assessments for each domain, with clear improvement plans and reporting.

• Design the enterprise data catalog operating model, covering registration, certification, curation, deprecation, and the asset lifecycle from discovery to publish.
• Define standards for metadata enrichment—classifications, sensitivity tags, custom attributes, ownership, and business context—and the operational model that keeps them current.
• Establish data lineage standards (technical, business, and process lineage) and the coverage targets that make lineage usable for impact analysis, audit, and change management.
• Lead tool selection or rationalisation when required, partnering with architecture, procurement, and engineering on evaluations and implementation.

• Design the business glossary taxonomy—term hierarchies, synonyms, approval workflows, and term‑to‑asset linkage standards.
• Lead the identification, definition, and classification of Critical Data Elements (CDEs) across priority domains, including ownership, authoritative source, allowable values, and downstream consumption mapping.
• Define data quality dimensions, rules, thresholds, and SLAs tied to CDEs, and ensure quality outcomes are visible to consumers in the catalog and in business reporting.
• Establish the issue management process: detection, triage, root‑cause analysis, remediation, and prevention.

• Define the governance OKRs and KPI framework, with quarterly targets that measure adoption, coverage, quality, and risk reduction.
• Example OKRs you will own and evolve: e.g., “Certify 100% of Tier‑1 data products in the enterprise catalog,” “Achieve 95% CDE coverage with assigned owners and DQ rules across priority domains,” “Reduce mean‑time‑to‑find trusted data by 50%,” and “Remediate 90% of high‑severity sensitive data exposures within SLA.”
• Build executive‑ready reporting and dashboards that track governance maturity, domain performance, and business outcomes.
• Articulate the governance value story — risk reduction, decision quality, productivity, and cost — in terms that resonate with executive and non‑technical audiences.

• Partner with privacy, security, and risk functions to ensure governance…