Position Summary We are seeking an AI Data Quality Engineer to improve trust, accuracy, and usability of Clinical GPS data through data quality engineering, analytics, automation, and machine learning-based monitoring. This role will focus on identifying anomalies, drift, missing data, coding inconsistencies, duplicate records, and attribution risks across clinical and operational datasets.

Key Responsibilities Design and implement data quality checks, validation rules, profiling routines, and automated monitoring processes. Use SQL, Python, and ML/AI techniques to detect anomalies, data drift, missing values, duplicates, and inconsistent coding patterns. Collaborate with data engineering, analytics, product, clinical, and governance teams to define quality rules and remediation workflows.

Support data governance, metadata management, lineage review, and issue tracking processes. Create dashboards, reports, and alerts that communicate data quality trends and operational risks. Recommend process improvements to reduce recurring data defects and improve confidence in Clinical GPS insights.

Required Qualifications Bachelor’s degree in Data Analytics Computer Science, Information Systems, Statistics, or related field.



6+ years of experience in data analytics data quality data engineering or healthcare data operations. Strong hands-on experience with SQL Python, data quality frameworks ML/AI techniques, and data governance practices.

Experience analyzing large datasets and identifying quality issues, patterns and root causes. Robust communication skills with the ability to explain data issues to technical and business stakeholders Preferred Qualifications Experience in healthcare analytics, payer data, provider data clinical data or population health datasets Familiarity with healthcare coding, member attribution, clinical quality measures and interoperability data Experience with automated quality monitoring, model-driven anomaly detection and dashboarding tools.

Key Skills SQL Python Data Quality ML/AI Data Governance

- Data Profiling
- Anomaly Detection Data Drift Monitoring
- Root Cause Analysis
- Healthcare Analytics Data Stewardship.

Success Measures

Improved accuracy completeness, and reliability of Clinical GPS datasets Early detection of data anomalies, drift, duplicates, and quality risks Reduced recurring data defects through root cause analysis and process improvements. Clear data quality reporting adopted by business analytics and delivery teams

📌 Architect (ATC) (Chennai)
🏢 Virtusa
📍 Chennai