The Data Management Specialist will play a crucial role in supporting the Data Management Lead within the Enterprise Architecture & Data Management team. This position is designed for a tech-savvy individual who is adaptable and comfortable with Python, with a strong inclination towards governance. The primary purpose of this role is to ensure the integrity, quality, and governance of data products, metadata, and data lineage within the organization, contributing to the successful adoption of a data mesh architecture. Main activities: Data Management stream Collaborate closely with the Data Management Lead to implement and uphold best practices in data management and governance, facilitating the transition to a data product approach within the data mesh architecture. Support data management implementation for all Data stakeholders: Data Owners, IT Product or Application Owners, Data Engineers and Data Analysts, Enterprise Architecture. Control data products and ensure their quality and compliance with governance standards. Interact with various lines of business and regional teams to gather requirements and provide support. Ensure the accuracy and completeness of metadata in the data catalog and maintain the quality of both manual and automatic data lineage. Manage and monitor Data Management tooling and technical processes: MetaData Catalog (OpenMetadata), Data Quality Management (Dataiku), Data Pipeline quality checks, Metadata tools and pipelines (Dataiku and Python), Data Product Catalog (DataMesh Manager) Develop and implement automated solutions for data management processes e.g., by developing custom scripts and tools using Python to enhance data governance and quality. Data Quality Stream Support Data Management Lead as a referent to provide support and expertise to guarantee Data quality, at Global and Data Domain levels. In collaboration with the Datalayer Management team, contributes define and build standard data quality tooling: unit tests, end to end monitoring, post processing quality control and dashboarding. In collaboration with the Data Engineering teams (Corp, Lob & Region) and Data owners, take part in set up data quality metrics, check and dashboards and monitors data quality through the standard tooling.