Global Master Data Sr. Analyst H/F

Master Data Governance & Standards

• Manage, analyze, validate, and maintain master data within assigned business domains
• Support master data governance processes, standards, and data quality controls
• Identify and resolve data issues such as duplicates, inconsistencies, missing attributes, and formatting errors
• Support Deployment of AI-assisted tools for routine data quality monitoring, anomaly detection, duplicate detection, attribute standardization, classification harmonization, and obsolete material management; establish automated audit cycles that continuously assess completeness and accuracy against global standards.
• Monitor data quality KPIs and prepare reports for governance and business stakeholders
• Support data creation, change, approval, and retirement workflows
• Collaborate with ERP, EAM, MDM, and other enterprise system teams to ensure data consistency across platforms
• Document master data definitions, business rules, workflows, and governance procedures
• Support data migration, system implementation, and integration projects
• Ensure compliance with internal governance standards, audit requirements, and applicable regulatory expectations
• Contribute to continuous improvement initiatives to strengthen enterprise data quality and process efficiency
• Partner with Process Engineers to ensure process standards and master data templates evolve together; develop and maintain global training materials and documentation for both global and local users.

GPDA Authority & LDPA Network

• Work closely with LPDAs, data stewards, and system owners to resolve data defects and improve processes
• Drive the LDPAs governance rhythm: regular cadence for standards updates, compliance reviews, issue escalation, and cross-site read-across of lessons learned, including cross-functional alignment with Engineering/Configuration Management on digital thread policy changes impacting PLM–ERP integration.

Analytics Rationalization & Incubation

• Establish global reporting standards and data definitions to ensure consistent interpretation of metrics and KPIs across all sites and functions.
• Build and validate analytics prototypes with frontline teams (including AI-powered data quality and operational analytics tools) proving concepts at small scale before handoff to IT/DT for industrialization.
• Define and own the prototype-to-scale handoff protocol: documentation standards, data requirements, and acceptance criteria that IT/DT teams need to industrialize a DPE-incubated tool.