Senior Data Governance Manager - Automation & Insights
Role details
Job location
Tech stack
Job description
You will be responsible for advancing our data governance framework to the next level of maturity. This includes driving metadata excellence, ensuring trusted lineage, embedding governance into AI workflows, and-critically-establishing governance standards for both data models and the enterprise semantic layer that unify how data is described, discovered, and consumed. Your leadership will ensure that every data model, every AI model, every automation, and every insight is powered by high-quality, explainable, and well-governed data., 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., Identify and raise any non-compliance incidents promptly to your line manager. Challenge processes, policies and projects that will negatively impact compliance within the Group. Complete all mandatory compliance training assigned to you. Reach out to the Compliance Teams if unsure of any of your compliance obligations or the requirements are unclear.
Our Way Of Working
Our world is hybrid.
A career is not a sprint. It's a marathon. One of the perks of joining us is that we value you as a person first. Our hybrid world allows you to focus on your goals and responsibilities and lets you self-organise to improve your deliveries and get the work done in your own way.
Requirements
Do you have experience in SQL?, 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.
