Data Governance Manager

We are seeking a delivery-focused and pragmatic Data Governance Manager to establish, embed and scale data governance across the JD Group. This is a newly created role, reporting into the Head of Data Architecture, and will play a critical role in building the trusted, well-governed data foundations required to support analytics, regulatory compliance, and the responsible, ethical use of data within AI-driven innovation.

You will be responsible for translating enterprise-level data architecture and governance strategy into practical, adopted governance operating models, starting with the Finance data domain and progressively expanding across the wider business. While the role has a strong strategic remit, it is explicitly hands-on, particularly in its early stages, with responsibility for designing frameworks, configuring tooling, and driving adoption directly.

The role will play a key part in ensuring that data used to train, power and operate AI products is high quality, transparent, well-controlled and ethically sourced, aligned to JD’s AI governance principles. Over time, the role will help shape and grow a wider data governance capability, contributing to the development of a group-wide data culture where ownership, quality and trust are embedded by default.

• Data Governance Strategy & Frameworks
  • Own the design and implementation of JD’s data governance approach in alignment with the Group Data Architecture vision and standards.
  • Define pragmatic governance frameworks covering data ownership and stewardship, critical data elements and data classification, metadata, lineage and transparency and data quality management and controls.
  • Ensure governance frameworks are scalable, repeatable and proportionate, enabling delivery rather than slowing it down.
  • Contribute to the evolution of group-wide data architecture and governance standards and playbooks.
• Ownership, Stewardship & Operating Model
  • Establish and embed a clear data ownership and stewardship model, initially within the Finance domain.
  • Work closely with Finance stakeholders to formalise roles, responsibilities and accountability for data.
  • Create operating models, playbooks and guidance that can be reused across additional data domains.
  • Act as a trusted advisor and coach to data owners and stewards, supporting capability uplift across the business.
• Tooling, Metadata & Lineage
  • Lead the implementation and adoption of data governance tooling, including Dataplex (GCP) for technical governance within the data platform and Alation as the enterprise data catalogue and lineage solution.
  • Define and enforce standards for metadata, lineage and certification of trusted data assets.
  • Partner with Data Architecture and Data Engineering teams to ensure governance is embedded into data platform design, data pipelines and models and analytics and reporting assets.
  • Ensure AI-relevant datasets, features and derived data products are fully catalogued, classified and traceable within governance tooling to support transparency and explainability.
• Data Quality, Trust & Retention
  • Define JD’s approach to data quality management and data retention, aligned to architectural standards and business priorities.
  • Work with business and technical teams to identify critical data assets and agree quality expectations.
  • Establish and embed JD’s data retention policy agreeing a prioritised roadmap with technical stakeholders for implementation.
  • Enable transparency of data quality metrics and lineage to build confidence in analytics, reporting and AI use cases and support remediation of data quality issues through clear ownership and prioritisation.
  • Define heightened data quality, completeness and monitoring expectations for datasets used in AI and automated decision-making use cases.
• AI Data Governance & Ethics
  • Ensure that data used to train, power and operate AI and advanced analytics use cases is well-governed, high quality, transparent and ethically used.
  • Partner with Data Science, AI and Product teams to embed data ownership, lineage, quality and bias considerations into AI design and delivery.
  • Provide data governance input into AI approval and assurance processes, ensuring AI use cases are supported by trusted and well-controlled data.
• Governance, Risk & Compliance
  • Support the Head of Data Architecture in embedding enterprise-grade governance, security and compliance across the data estate.
  • Ensure governance practices align with data security, privacy, regulatory and ethical requirements, including where data is used in AI and automated decision-making.
  • Contribute to architectural reviews and design governance where data standards and controls are required.
• Stakeholder Engagement & Change
  • Act as the primary point of contact for data governance across JD.
  • Build strong relationships with Technology, Finance and wider business teams to drive engagement and adoption.
  • Clearly communicate the value of data governance to both technical and non-technical audiences.
  • Drive cultural change so that governance becomes part of “how we work” rather than a separate activity.
• Leadership & Capability Development
  • Operate initially as a senior individual contributor, delivering tangible outcomes hands-on.
  • Define the future shape of the Data Governance capability and support the Head of Data Architecture in scaling the function.
  • Contribute to the recruitment, onboarding and development of future data governance roles.
  • Promote a strong data culture, ownership mindset and continuous improvement ethos.

• Clear, adopted data ownership and stewardship within Finance.
• High-value data assets catalogued, discoverable and trusted via Dataplex and Alation.
• Improved transparency of data lineage and data quality across priority datasets.
• A scalable governance operating model ready to be rolled out across additional domains.
• Data governance embedded into architecture, platform and delivery processes.
• Clear governance, ownership and quality standards established for priority datasets.
• Strong transparency and auditability of data assets, enabling compliance and responsible AI use.
• Governance viewed as an enabler of better decisions and faster delivery.

• Significant senior-level experience implementing data governance in complex, evolving organisations including experience of introducing this and building governance from the ground up.
• At least five years’ experience driving the adoption of data governance principles within large, multifaceted organisations.
• Strong practical understanding of data governance concepts including ownership, stewardship, metadata, lineage and data quality.
• Hands-on experience with modern data governance or cataloguing tools (e.g. Alation, Dataplex, Collibra, Informatica or similar).
• Experience supporting data governance for advanced analytics or AI use cases, including understanding of data ethics, bias, transparency and explainability considerations.
• Experience working with cloud-based data platforms, ideally GCP.
• Ability to operate effectively across strategy, delivery and change.
• Strong stakeholder management and influencing skills, including working without formal authority.
• Effective communicator who can influence and engage senior stakeholders across business and technology domains who can provide authoritative guidance.
• Ability to simplify and demystify data governance, metadata, lineage and retention concepts to drive understanding and adoption across the business.