What is the Opportunity?
The Manager of Data Governance is a pivotal leadership role within our organization, tasked with establishing and maintaining robust data stewardship practices that underpin our Data Operations (DataOps) strategy. This position ensures that essential data is seamlessly integrated into our enterprise systems, ensuring accuracy and completeness across the organization. The ideal candidate will be an advocate for data-driven decision making, an effective collaborator across IT and business domains, and a steward for the integrity, accessibility, and usability of enterprise data.
What You’ll Do Here :
- Data Stewardship Leadership : Develop, operationalize, and continuously improve data stewardship practices and frameworks to support the full data lifecycle, with specific attention to DataOps methodologies and principles.
- Critical Data Element Management : Oversee the identification, seamless data integration, and ongoing management of critical data elements (CDEs) throughout the enterprise. Ensure these CDEs meet quality and compliance standards.
- Quality Assurance and Screening : Implement robust data screening protocols to validate data for accuracy, completeness, consistency, and readiness for downstream use.
- Establish automated and manual checks and collaborate with quality assurance teams to remediate data issues.
- Collaboration with IS Data Engineering : Serve as a primary liaison to Information Systems (IS) Data Engineering teams. Define requirements for data integration pipelines, data transformation, and storage architectures that facilitate effective governance and stewardship of enterprise data.
- Business Partnership : Work closely with business units and data owners to understand operational data requirements, pain points, and business objectives.
- Facilitate alignment between data management practices and strategic business goals.
- SAP Transactional Data Governance : Oversee SAP transactional data governance, ensuring high-quality master and transactional data across SAP modules. Lead data cleansing, enrichment, and harmonization initiatives to optimize business processes and reporting.
- Enterprise Semantic Data Model Development : Lead the creation and maintenance of the enterprise semantic data model that underpins strategic data products. Ensure that this model is comprehensive, scalable, and reflects the nuances of business processes, terminology, and analytics needs.
- Policy and Standards Development : Define, document, and socialize data governance policies, standards, and operating procedures. Champion adoption across the enterprise and ensure compliance through regular audits and reviews.
- Metrics and Reporting : Develop and monitor key performance indicators (KPIs) for data quality, stewardship effectiveness, and compliance. Provide regular status updates and insights to executive leadership and key stakeholders.
- Change Management and Training : Drive change management initiatives to foster a culture of data stewardship and accountability. Design and deliver education programs and training sessions for data stewards, owners, and business users.
- Team Management : Lead, mentor, and develop a team of data analysts. Provide guidance, coaching, and professional development to foster expertise in modern data governance and analytical techniques
What You Bring to the Team :
- Bachelor’s degree in Engineering, Information Systems, Computer Science, Data Science, Business Administration, or a related field. Master’s degree preferred.
- 5+ years of experience in data governance, data management, data stewardship, or related disciplines within complex enterprise environments with at least 2 years in a leadership or managerial capacity.
- Deep familiarity with DataOps principles, tools, and best practices.
- Hands‑on experience with SAP ERP systems, especially in managing master and transactional data quality and governance.
- Proven experience collaborating with data engineering, analytics, and business teams.
- Knowledge of enterprise data modeling, particularly semantic data modeling for analytics and data products.
- Strong understanding of data quality frameworks, data lineage, metadata management, and data cataloging tools.
- Excellent communication and stakeholder engagement skills, with the ability to translate technical concepts for non‑technical audiences.
- Experience building and scaling data stewardship frameworks in regulated industries is a plus.