Data Quality Supervisor

Key Responsibilities

• Lead the implementation and maintenance of data quality standards, policies, procedures, and controls for critical enterprise data.
• Establish and maintain data quality rules and validation criteria covering accuracy, completeness, consistency, uniqueness, validity, and timeliness.
• Oversee data profiling, assessment, validation, and monitoring of critical provider data.
• Monitor and report data quality performance through KPIs, scorecards, dashboards, and management reports.
• Lead the investigation and resolution of data quality issues through root-cause analysis and corrective/preventive actions.
• Oversee data reconciliation and validation across source systems, standardized datasets, and Oracle.
• Collaborate with Data Engineering teams to automate data quality checks, validation, reconciliation, and monitoring processes.
• Define and monitor quality criteria for gold-standard data and ensure critical datasets meet established standards.
• Partner with data owners and cross-functional teams to resolve data quality issues at the appropriate source.
• Supervise, coach, and develop Data Quality Analysts while driving continuous improvement and reducing manual quality activities.

Required Experience

• 1–3 years of experience in data quality, data management, data analytics, master data management, or a related field.
• Hands-on experience in data profiling, validation, cleansing, reconciliation, and data quality monitoring.
• Experience developing and implementing data quality rules, controls, KPIs, and reporting.
• Experience working with large or complex datasets from multiple data sources.
• Experience conducting root-cause analysis and resolving recurring data quality issues.
• Experience in data quality automation or process improvement is highly preferred.
• Experience working with ETL/ELT processes and data integration is preferred.
• Experience using data visualization and reporting tools, such as Power BI or similar platforms, is an advantage.

Qualifications & Skills

• Strong knowledge of data quality principles, data governance, and master data management.
• Advanced skills in data profiling, validation, reconciliation, and analysis.
• Knowledge of data modeling, metadata, data lineage, and data standards.
• Proficiency in Python or other scripting languages for data quality automation is preferred.
• Familiarity with data quality tools, automation, and monitoring solutions.
• Familiarity with healthcare data is an advantage.
• Strong analytical and problem-solving skills, with the ability to identify issues and determine root causes.
• Strong leadership skills with the ability to supervise, coach, and develop team members.
• Excellent communication and collaboration skills across teams and functions.
• Strong attention to data accuracy, integrity, and quality.
• Proactive mindset with a focus on continuous improvement and process optimization.
• Ability to translate data quality issues and insights into clear recommendations and business actions.
• Strong sense of ownership and accountability for quality outcomes and issue resolution.