Privia Health is hiring a Sr. Data Quality Analyst

The Sr. Data Quality Analyst plays a key role in the testing and implementation of advanced reporting and analytics/tools to support the Data Foundry team. Working closely with other team members to design, build and maintain scalable data quality frameworks to validate accuracy, completeness, consistency and timeliness of enterprise data across multiple data domains Primary 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.