Data Quality Analyst

Position Summary

The Data Quality Analyst plays a crucial role in ensuring the integrity, accuracy, and reliability of data across our OEM organization. Initially, the focus is on validating and cleansing order and master data to support immediate reporting needs. The long-term aim is to transform into a strategic role that builds scalable validation processes, automation, and governance—impacting forecasting, invoicing, BOMs, pricing, and finance.

Key Responsibilities

Short-Term / Tactical

• Validate customer order and master data across ERP and reporting systems for accuracy and consistency.

• Identify and correct errors, duplicates, missing fields, and false entries.

• Collaborate with teams (customer service, finance, planning) to resolve data issues at their source.

• Produce clear, actionable discrepancy reports and share key findings with leadership.

Long-Term / Strategic

• Design and implement scalable, automated validation workflows and error-prevention routines.

• Build dashboards and data integrity metrics using ERP, BI tools (e.g., SAP, Power BI) for visibility and accountability.

• Define and drive data governance practices and standards with cross-functional adoption.

• Conduct root-cause analysis for recurring data errors and implement process improvements.

• Document data processes, maintain data definitions and SOPs, and train stakeholders on new workflows.

Qualifications

• Bachelor’s degree in Data Analytics, Supply Chain, Business, or a related field (or equivalent experience).

• Strong Excel skills (pivot tables, lookups, formulas, Power Query). Experience with SQL and ERP systems preferred.

• Familiarity with BI/reporting tools (Power BI, Tableau) is a plus.

• Sharp analytical skills with solid attention to detail.

• Excellent interpersonal and communication skills—able to collaborate across departments effectively.

• Lean/Six Sigma or continuous improvement mindset is advantageous.

Success Metrics

• Data discrepancies are consistently identified, resolved, and prevented.

• Automated validation processes and governance frameworks are implemented and adopted organization-wide.

• Leadership can track and understand data quality through dashboards and reports.

• The role transitions from tactical cleanup to proactive, data-driven process control.