Reliable data requires more than a data catalog and a policy document. It demands clear ownership, shared definitions, and effective controls, especially when that same data is used for AI. As Head of Data Governance, you lead the further development and implementation of governance within a complex organization. You build an approach that enables teams to use data reliably, traceably, and responsibly.
You lead specialists in governance, metadata, data quality, business data analysis, data architecture, and data modeling.
What you do
- Develop the governance strategy and roadmap, translating them into concrete priorities, measurable results, and working agreements.
- Establish roles, mandates, decision-making authorities, and escalation paths.
- Support Data Owners, Data Stewards, and product managers in their roles and ensure governance meetings lead to decisions.
- Develop business glossaries, data catalogs, lineage, and the management of critical data elements.
- Ensure quality issues are visible, owned, and structurally resolved.
- Strengthen the governance of AI applications: from intake, classification, and prioritization to monitoring and lifecycle management, focusing on ownership, transparency, explainability, privacy, and responsible use.
- Embed standards and controls in data products, platform onboarding, and development processes.
- Develop a roadmap for a metadata and governance platform that supports data and AI applications.
- Coach the team and connect business, data, IT, architecture, risk, compliance, and legal.
- Report on progress, adoption, data quality, and the effectiveness of data and AI governance.
- Ensure data architecture and data modeling contribute to governance goals and align with existing architectural principles and standards.
What they ask
- Minimum 7–10 years of relevant experience in Data Governance, data management, metadata, data quality, or similar leadership roles.
- Demonstrable experience in designing and implementing governance frameworks, operating models, roles, standards, and controls.
- Thorough knowledge of metadata management, business glossaries, data catalogs, lineage, critical data elements, and data quality.
- Understanding of how ownership, privacy, metadata, and data quality contribute to reliable AI.
- Sufficient knowledge of data architecture, data modeling, data platforms, and cloud to translate governance requirements into execution.
- Experience in leading and developing multidisciplinary teams.
- A relevant university degree, for example in information management, computer science, economics, or finance.
- Experience within a complex financial environment is a plus.
- Experience with AI Governance, supervision of the lifecycle of models or AI applications, and certifications such as DAMA/DMBOK are also a plus.
- Ability to interact with senior management and ask in-depth questions to specialists.
- Ability to clarify responsibilities and keep the approach workable, without unnecessary bureaucracy.
Practical
- Engagement via secondment.
How to apply
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