Master Data Analyst

Master Data Analyst

The Master Data Analyst is responsible for supporting the creation, maintenance, and overall quality of core business data across the organization. This role ensures that master data is accurate, consistent, and aligned with established governance standards to enable efficient business operations and reliable reporting.

Requirements:

Key Responsibilities

• Create, update, and maintain master data records in ERP and related business systems.

• Perform routine data validation, cleansing, and enrichment to ensure accuracy and completeness.

• Support data governance processes by following established standards, naming conventions, and approval workflows.

• Identify data quality issues and collaborate with cross‑functional teams to resolve them.

• Monitor data integration between systems (ERP, CRM, PLM, etc.) and escalate discrepancies.

• Generate reports and dashboards to track data quality metrics and operational performance.

• Document processes, data definitions, and standard operating procedures.

• Participate in master data projects, system enhancements, and testing activities.

• Provide basic support to end users regarding data requests, corrections, and troubleshooting.

Qualifications

• Bachelor's degree in Information Systems, Business, Data Analytics, or a related field (or equivalent experience).
• Strong analytical and problem‑solving skills with a high level of accuracy.
• Familiarity with data management concepts such as data quality, data governance, and master data.
• Proficiency with Micosoft Excel, SQL, or data visualization tools (Power BI, Tableau).
• Exposure to ERP systems (SAP, Oracle, NetSuite, etc.).
• Experience with master data software (STIBO Systems).
• Ability to work collaboratively, manage multiple tasks, and communicate clearly with stakeholders.

Preferred Skills

• Understanding of relational databases and data structures.
• Experience with data cleansing or data migration activities.
• Interest in developing expertise in MDM tools and data governance frameworks.
• Curiosity, willingness to learn, and a proactive approach to improving data processes.

What Success Looks Like

• Consistent, accurate, and timely updates to master data.
• Improved data quality metrics and fewer downstream data issues.
• Clear documentation and adherence to governance standards.
• Growing independence and technical capability over time.