Senior Data Governance Manager – Automation & Insights in London

About the Company

At FDJ UNITED, we don’t just follow the game, we reinvent it. FDJ UNITED is one of Europe’s leading betting and gaming operators, with a vast portfolio of iconic brands and a reputation for technological excellence. With more than 5,000 employees and a presence in around fifteen regulated markets, the Group offers a diversified, responsible range of games, both under exclusive rights and open to competition. We set new standards, proving that entertainment and safety can go hand in hand. Here, you’ll work alongside a team of passionate individuals dedicated to delivering the best and safest entertaining experiences for our customers every day. We’re looking for bold people who are eager to succeed and ready to level-up the game. If you thrive on innovation, embrace challenges, and want to make a real impact at all levels, FDJ UNITED is your playing field. Join us in shaping the future of gaming. Are you ready to LEVEL-UP THE GAME?

About the Role

We are a global leader in data-driven transformation, harnessing AI and automation to power decisions, create innovative products, and deliver trusted customer experiences. Our mission is to build an intelligent, self-service data ecosystem where automation, explainability, and trust are embedded at every layer. As we accelerate into the era of generative AI, data governance is no longer just about compliance and stewardship — it is about enabling safe, explainable, and scalable innovation. We are now seeking a Senior Data Governance Manager to drive this.

Responsibilities

• Governance Leadership & Strategy
  • Define and evolve a modern data governance vision that aligns with our Data, AI and automation strategy, enabling innovation while maintaining compliance and trust.
  • Partner with senior stakeholders across Product, Engineering, and A&I to ensure governance accelerates—not slows—value creation.
  • Champion a “governance-as-enabler” culture, where automation, explainability, and trust are seen as strategic advantages.
• Semantic Layer & Data Model Stewardship
  • Establish enterprise-wide standards and definitions for the semantic layer (e.g. Cube.js, knowledge graphs).
  • Govern the creation and evolution of business data models, master data, and metric definitions, ensuring consistency and alignment across domains.
  • Ensure data models are tightly integrated with metadata catalogues and lineage tools for discoverability and explainability.
  • Partner with Analytics, Engineering and Platform teams, who build data models and semantic layers, to guarantee alignment, scalability, and AI readiness.
  • Ensure semantic and data models underpin generative AI use cases such as natural language querying, text-to-SQL, and conversational exploration.
  • Conduct governance maturity assessments measuring semantic layer adoption, metadata quality, and AI-readiness across teams.
  • Drive continuous improvement roadmaps that automate governance workflows and accelerate trusted data delivery.
• AI & Generative AI Governance
  • Embed governance into AI pipelines, ensuring models consume only trusted, explainable, and lineage-rich data models.
  • Partner with AI/ML teams to ensure governance supports LLMOps, RAG (retrieval-augmented generation), and agent-based AI systems.
  • Define guardrails for how data and semantic models are used in generative AI to ensure transparency, ethics, fairness, and reliability.
• Metadata & Data Catalogue Excellence
  • Oversee the enterprise data catalogue and lineage systems, ensuring end-to-end transparency across ingestion, transformation, and consumption layers.
  • Ensure the data catalogue provides rich, connected metadata linking datasets, semantic models, AI models, pipelines, and policies in a way that both humans and AI agents can interpret.
  • Automate data quality monitoring and lineage capture to support explainable AI and regulatory requirements.
  • Enable governed self-service access to data, ensuring business users can confidently explore and analyse data within defined guardrails.
  • Enable governed self-service analytics through the semantic layer, balancing user agility with automated controls.
  • Ensure governance policies embed seamlessly into A&I data platform, making compliant access the default path.
• Policy, Compliance & Risk Management
  • Define and enforce governance policies covering access, lifecycle management, security, and privacy.
  • Translate regulatory obligations (e.g., GDPR, industry-specific compliance) into actionable, automated controls within the platform.
  • Align governance with established frameworks (e.g., DAMA-DMBOK) and embed best practices in stewardship programmes.
  • Ensure governance frameworks scale across modern data platforms (e.g., AWS) and integrate seamlessly with engineering workflows.

Qualifications

• Proven experience in data governance, data management, or data quality leadership roles.
• Strong understanding of data governance frameworks (e.g., DAMA-DMBOK), metadata, data quality, and master data management (MDM).
• Demonstrated expertise in data ethics and regulatory compliance frameworks such as GDPR, with a strong ability to translate these principles into practical data policies and controls.
• Strong understanding of modern data platforms and architectures (e.g., AWS) and how governance integrates with engineering workflows.
• Hands-on experience with data cataloguing, metadata management, and lineage tools (e.g., Collibra, Alation, OpenMetadata).
• Familiarity with semantic layer concepts and tools (e.g., Cube.js, LookML) and their role in governing metric definitions, data contracts, and AI/analytics consumption.
• Experience contributing to or leading data stewardship programmes and fostering a culture of ownership and accountability.
• Familiarity with self-service analytics environments and enabling governed access to data for business users.
• Excellent communication and stakeholder engagement skills, with the ability to influence across technical and business functions.
• Relevant certifications such as CDMP (Certified Data Management Professional) or equivalent credentials in data governance and management are preferred.