Data Governance Manager

Data Governance Manager   Role Overview:   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.     Responsibilities:   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 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     Role Objectives & KPIs 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   Skills and Experience: 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