Data Governance Lead Hybrid

Define Data Governance mission and purpose for Data & Analytics, and translate it into a DG strategy and roadmap aligning with company objectives.

Collaborate with cross-functional teams and stakeholders to cultivate a culture of data governance, encouraging proactive engagement and participation from all team members. Increase awareness of risks by integrating ongoing risk assessments and training initiatives. Strengthen audit trailing and data lineage monitoring by implementing agile methodologies and tools for real-time tracking and analysis.

Ensure the systematic collection and alignment of Data Protection requirements for all data entrusted to Data & Analytics platforms and systems. This includes selecting the necessary Data Protection controls, closely collaborating with Thndr’s Legal, compliance & Safety, and considering other protection requirements, such as competitive data sensitivity. Oversee the planning and implementation of these controls across all data products, platforms and services, as well as within the organization's Cyber Security function.

The Data Governance (DG) lead ensures the reliability of data by defining standards, conducting assessments, implementing controls, and fostering continuous improvement.

Advise and support various business areas (Data Custodians and Data Stewards) on mitigation and resolution of DQ issues resulting from lack of master data management or source data challenges in the business.

Ensure semantic alignment and consistency of information matrices, data models, metric and KPI definitions across the whole Enterprise through the relevant Data Governance forums, business Data Stewards and Data Custodians.

Ensure systematic context, definition and documentation of all relevant metadata for Enterprise Data & Analytics platforms and systems across all EDA products and services.

Stay connected with EDA's product and services roadmap as well as industry trends and developments in the areas of Data Governance, Data Management and Analytics. Define analytics self-service model for business users across the organization, i.e. different governance and DQ levels for business self-service, etc.

Manage and prioritize DG related feature backlog (Data Protection controls, metadata changes, etc.) on behalf of the business for implementation in Enterprise Data & Analytics platforms and systems, across different products and services, in close collaboration with associated Product community.

Build productive relationships with other capability heads such as Architecture, Delivery, Product Management, Data Engineering, Data Science, Business Intelligence, DevOps, Quality Assurance, tool providers and business stakeholders like Data Custodians and Data Stewards to enable collaboration at scale and effective resolution of any impediments encountered by the autonomous teams.