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.

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,…