Manager – Data Quality, Automation

Responsibilities

• Define and lead the data quality strategy for supply chain data domains (materials, suppliers, inventory, orders, logistics, and master data).
• Establish data quality standards, metrics, and KPIs, and build dashboards to monitor data health across systems.
• Implement data quality controls — profiling, validation rules, cleansing, and remediation workflows — across the data lifecycle.
• Partner with data governance, data engineering, and business stakeholders to identify root causes of data issues and drive sustainable fixes.
• Manage data quality issue resolution, including triage, prioritization, and tracking to closure.
• Champion a culture of data ownership and accountability, working with data stewards and domain owners across the supply chain organization.
• Support strategic initiatives and ensure alignment between operational systems and analytics platforms.
• Drive continuous improvement through automation, AI implementation, and adoption of data quality best practices.

• Bachelor's degree in Information Systems, Computer Science, Supply Chain, Data Science, or a related field.
• 10+ years of experience in data management, data quality, data governance, or analytics, with at least 4 years in a leadership or managerial capacity.
• Proven experience defining and operationalizing data quality frameworks, metrics, and automated cleansing solutions.
• Demonstrated experience applying AI/ML or advanced automation to data quality or data management challenges.
• Strong knowledge of data quality tools (e.g., Informatica Data Quality, Collibra, Talend, SAP MDG, or similar).
• Proficiency with SQL and experience working with large, complex datasets.
• Track record of driving efficiency and process improvement in a data-intensive environment.
• Excellent stakeholder management, communication, and problem-solving skills.
• Preferred:
  Experience in pharmaceutical, healthcare, medical device, or other regulated industries.
• Familiarity with SAP, ERP systems, and cloud data platforms (e.g., Azure, AWS, Snowflake, Databricks).
• Experience with AI-enabled data quality or data observability platforms.
• Knowledge of data governance frameworks (e.g., DAMA-DMBOK).
• Relevant certifications (CDMP, Six Sigma, or data quality/governance credentials).