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
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