Head of Data Management

Recruiting a Head of Data Management requires a thorough understanding of the role. The following is a very general summary, which should be adapted to your specific context.

The Head of Data Management reports directly to the Head of Data Governance. They act as the link between the company’s data governance strategy and its operational implementation through high-performance technological tools. Their role is to select, deploy, optimize, and industrialize data management solutions (Master Data Management, data dictionaries, catalogs, quality tools, etc.) in order to structure, secure, and enhance the company’s data assets. Working closely with business units, technical teams, and management, they ensure that data is reliable, accessible, and compliant, while supporting the transition to a truly data and AI-driven organization.

Core Responsibilities

1. Define and Lead the Data Management Tools Strategy

Develop and execute a technological roadmap to equip the company with the best tools for data management.
Evaluate, select, and deploy MDM, data catalog, quality, and lineage solutions, ensuring seamless integration into the ecosystem.
Collaborate with data stewards, business managers, and IT teams to set priorities and oversee deployments.

2. Oversee the Integration and Interoperability of Solutions

Ensure that tools such as Collibra, Informatica, Alation integrate effectively with ERP, CRM, data lakes, and other systems.
Define clear processes for metadata management, lineage, and quality rules.
Guarantee that solutions meet performance, security, and compliance requirements while remaining scalable.

3. Support Adoption and Skill Development

Design and deliver training and awareness programs tailored to different team maturity levels.
Promote adoption by tracking usage, satisfaction, and error reduction metrics.
Adapt communication to overcome resistance to change and ensure effective use of tools.

4. Ensure Data Quality and Compliance

Implement continuous data quality control mechanisms to maintain accuracy, completeness, and consistency.
Collaborate with the DPO and legal teams to ensure compliance with GDPR, BCBS 239, and internal policies.
Facilitate regulatory alignment while enabling optimal use of data.

5. Foster the Data Management Community and Promote a Data-Driven Culture

Organize workshops, reviews, and feedback sessions to prioritize needs and share best practices.
Build a shared vision of data as a strategic asset across business and technical stakeholders.
Promote a data-driven culture by showcasing the value of structured, well-managed data.

6. Measure Impact and Drive Continuous Improvement

Define and monitor KPIs (data quality, processing time, user satisfaction).
Adjust strategy and processes based on measurable results.
Stay updated on innovations (AI, automation, data mesh) to evolve tools and practices.

Examples of Concrete Achievements

Deployed an MDM solution (e.g., SAP MDG) to unify customer and product data, reducing errors by 30% and accelerating business processes.
Implemented a data catalog (e.g., Collibra) with a business glossary, improving accessibility and understanding for 500+ users.
Automated data quality controls (e.g., Talend) in pipelines, halving anomalies and boosting trust in data.
Trained teams on tools and best practices, ensuring rapid and effective adoption.