Responsibilities
- Leverage AI-Assisted data quality practices including - GenAI based rule generation and test case creation
- Intelligent anomaly detection and pattern recognition
- Automated Triage and Summarization of Data quality issues
- Develop and automate data quality checks and controls using SQL, Python in Snowflake environment
- Implement data validation, reconciliation and anomaly detection logic across batch and streaming data pipelines
- Embed Automated Data quality checks into ETL/ELT pipelines to enforce quality gates and prevent defective data from propagating downstream
- Build reusable data quality automation components, libraries and frameworks that can be consistently adopted across data engineering teams
- Comply with and contribute to departmental standards related to data, data governance, project planning, validation and documentation
- Validate BI datasets, semantic models and dashboards by ensuring: - Source-to-report data reconciliation - Metric and KPI accuracy - Aggregation, filter and refresh correctness
- Familiarity with value based care and fee for service delivery models
- Apply interoperability standards (HL7, X12 FHIR) to ensure systems communicate reliably and securely
- Monitor and report on data quality metrics, and trends, including dashboard level data accuracy and consistency
- Enable AIOps-style data observability using AI-driven insights to proactively identify data drift, schema changes and metric anomalies
- Support governance and audit readiness by ensuring data quality controls, validations, and dashboard certifications are documented and traceable
- Continuously improve data quality practices through automation, standardization and AI-driven enhancements, reducing manual validation effort
- Serve as a technical mentor and subject matter expert for the team, coaching newer engineers, driving knowledge sharing, establishing best practices, and promoting the adoption of AI-enabled data quality and automation frameworks across the organization
Requirements
- Leverage AI-Assisted data quality practices including GenAI based rule generation and test case creation
- Intelligent anomaly detection and pattern recognition
- Automated Triage and Summarization of Data quality issues
- Develop and automate data quality checks and controls using SQL, Python in Snowflake environment
- Implement data validation, reconciliation and anomaly detection logic across batch and streaming data pipelines
- Embed Automated Data quality checks into ETL/ELT pipelines to enforce quality gates and prevent defective data from propagating downstream
- Build reusable data quality automation components, libraries and frameworks that can be consistently adopted across data engineering teams
- Comply with and contribute to departmental standards related to data, data governance, project planning, validation and documentation
- Validate BI datasets, semantic models and dashboards by ensuring source-to-report data reconciliation, metric and KPI accuracy, aggregation, filter and refresh correctness
- Apply interoperability standards (HL7, X12 FHIR) to ensure systems communicate reliably and securely
- Monitor and report on data quality metrics, and trends, including dashboard level data accuracy and consistency
- Enable AIOps-style data observability using AI-driven insights to proactively identify data drift, schema changes and metric anomalies
- Support governance and audit readiness by ensuring data quality controls, validations, and dashboard certifications are documented and traceable
- Continuously improve data quality practices through automation, standardization and AI-driven enhancements, reducing manual validation effort
- Serve as a technical mentor and subject matter expert for the team, coaching newer engineers, driving knowledge sharing, establishing best practices, and promoting the adoption of AI-enabled data quality and automation frameworks across the organization
Nice to Have
- Familiarity with value based care and fee for service delivery models
