Enterprise Supply Chain Data Governance Manager

Role Overview

• Lead, develop, and coach a high‑performing team of Data Stewards, establishing clear objectives, feedback rhythms, and a culture of continuous learning.
• Set stewardship operating rhythms, including prioritization, sprint/PI cadences, OKRs, and performance metrics such as data quality and adoption.
• Own enterprise stewardship strategy and standards across supply chain domains, including glossary management, metadata, definitions, and reference/master data alignment.
• Oversee creation and maintenance of data quality rules, implementing automated controls to detect and prevent downstream issues.
• Ensure secure, compliant data access and communicate vendor updates, usage requirements, and contractual obligations.
• Partner with Digital Technology Solutions (DTS), Digital Product Management, and supply chain teams to translate business logic into governed, reusable data products and drive adoption.
• Advise product managers and data scientists on source suitability, business logic, and proper data usage; promote self‑service and training.
• Participate in governance forums, track adoption and value, and drive continuous improvement and automation of stewardship workflows.
• Manage change by communicating source and vendor updates, assessing downstream impacts, and keeping documentation and catalog entries current.
• Demonstrate leadership through vision‑setting, people development, cross‑functional influence, effective change management, sound decision‑making, and an inclusive, results‑oriented culture.

Requirements

• Bachelor’s degree in Supply Chain, Engineering, Computer Science, Data, or related field.
• 8+ years of overall professional experience in Data
• 3+ years leading data-related teams (Data Governance, Data Stewardship, Analytics, or Data Products).
• 3+ years working in Supply Chain environments (manufacturing, planning, logistics, procurement, or related).
• Proven experience building and scaling data governance and data stewardship ways of working from scratch.
• Strong expertise in data quality management, including: Definition of standards, Monitoring and validation of data and Identification of data gaps and inconsistencies across systems
• Experience working with large datasets and enterprise data platforms.
• Ability to define and maintain business glossaries, metadata, and data quality rules.
• Strong stakeholder management skills and ability to influence without authority.
• Fluent English (mandatory).

Benefits

• We believe that our employees are our greatest asset, and we're committed to providing them with the resources they need to be successful.
• Flexible work model that blends remote work with intentional in-person collaboration.