Data Lead - Data Governance / MDG

The company is looking for a Data Lead Data Governance / MDG to support the next phase of our enterprise data maturity across Manufacturing Operations. This role will partner with Enterprise Data Governance, BizApps, SAP/PLM owners, business data stewards, and cross-functional process owners to evaluate our current data management implementation and define the path toward a more scalable data governance capability.

The ideal candidate will bring hands-on experience with data governance operating models, data quality, workflow design, approvals, stewardship, and enterprise data platforms. SAP MDG experience is strongly preferred, but we are open to candidates with strong experience in comparable MDM/MDG platforms and governance implementation. This person will help conduct a structured gap analysis between current MDM implementation and the capabilities required from an enterprise MDG model, including workflow, ownership, approvals, data quality, auditability, stewardship, domain governance, and integration with downstream business processes., * Lead a current-state assessment of existing data management processes, tools, workflows, controls, and data governance practices.

• Conduct a gap analysis between current MDM capabilities and target-state MDG capabilities, including workflow, approvals, stewardship, audit trail, data quality, duplicate prevention, and governance controls.
• Evaluate SAP MDG capabilities and/or comparable MDG/MDG solutions against business needs, current SAP landscape, and manufacturing operations requirements.
• Partner with EDG, BizApps, SAP functional teams, PLM/3DX owners, and business stakeholders to define the target-state data governance operating model.
• Support business case development for MDG, including scope, value drivers, implementation approach, dependencies, risks, and phased roadmap.
• Define governance requirements across data domains such as material, MM/PP/QM, EWM, TM, FSM, CDC, supplier/vendor, customer/service, and other MOPS-relevant domains.
• Document data ownership, steward responsibilities, approval workflows, change-control requirements, and governance checkpoints.
• Identify opportunities to reduce manual data enrichment, improve data quality, standardize approvals, and improve auditability.
• Support prioritization of MDG use cases and implementation phases based on business impact, data readiness, process maturity, and system dependencies.
• Partner with technical and functional teams to translate business requirements into solution requirements for MDG/MDM implementation.
• Help define data quality rules, validation checks, duplicate detection requirements, reporting needs, and exception-management processes.
• Support workshops with business stakeholders to align on process design, ownership model, and implementation priorities.
• Create executive-ready documentation, recommendations, and roadmap materials for leadership review., Key Deliverables:
• Current-state assessment of existing MDM implementation and governance model.
• Gap analysis between current MDM capabilities and target-state MDG capabilities.
• MDG/MDM capability evaluation, including SAP MDG fit assessment.
• Recommended target-state data governance operating model.
• Prioritized MDG roadmap with implementation phases, dependencies, risks, and value drivers.
• Business requirements for workflow, approvals, data quality, stewardship, duplicate prevention, auditability, and reporting.
• Executive-ready recommendation for leadership review and decision-making.

Data Management, SAP, Manufacturing Data Management, Purchasing/Procurement, Logistics, Manufacturing, Manufacturing Operations, Cross-Functional, SAP PLM, Gap Analysis, * 8 to 10 years of experience in data management, data governance, enterprise data governance, SAP data, or related data management roles.

• Experience supporting or implementing enterprise MDM/MDG platforms; SAP MDG experience is strongly preferred.
• Strong knowledge of SAP data processes across one or more domains such as Material, MM, PP, QM, EWM, TM, FSM, procurement, manufacturing, logistics, quality, or service operations., * Bachelor’s degree with 8 to 10 years of experience in data management, data governance, enterprise data governance, SAP data, or related data management roles.
• Strong understanding of data governance concepts, including ownership, stewardship, workflow, approvals, data quality, auditability, and change control.
• Experience supporting or implementing enterprise MDM/MDG platforms; SAP MDG experience is strongly preferred.
• Strong knowledge of SAP data processes across one or more domains such as material, MM, PP, QM, EWM, TM, FSM, procurement, manufacturing, logistics, quality, or service operations.
• Experience conducting current-state assessments, gap analyses, capability assessments, and roadmap development.
• Ability to translate business process needs into data governance and system requirements.
• Strong cross-functional collaboration skills with business teams, IT/BizApps, data governance, SAP functional teams, and system owners.
• Experience facilitating workshops, documenting requirements, and building executive-ready recommendations.
• Strong analytical, problem-solving, communication, and stakeholder-management skills.
• Comfortable working in a fast-paced, ambiguous environment where processes and operating models are still maturing., * Hands-on experience with SAP MDG implementation, configuration, process design, or business ownership.
• Experience with SAP S/4HANA data governance and data lifecycle management.
• Experience in manufacturing, automotive, robotics, aerospace, supply chain, logistics, or high-tech manufacturing environments.
• Familiarity with PLM-to-SAP data flows, especially 3DX, BOM, part, material, and release-governance processes.
• Experience defining data domain models, data quality rules, duplicate prevention controls, and stewardship operating models.
• Experience supporting business case development for enterprise data platforms or governance capabilities.
• Familiarity with data cataloging, data lineage, metadata management, and data quality tooling.