Thank you for considering the Healthcare Data Quality Analyst position at Reveal Health Tech. We are a Series A–stage IT startup based in the US and India, delivering technology-driven solutions for the healthcare and life sciences industry as we scale toward our next phase of growth.
About the Role
We're looking for a Healthcare Data Quality Analyst who ensures that healthcare data is accurate, timely, and contract-grade. You’ll define data quality rules, reconcile across disparate sources, operationalize validation processes, and partner with provider groups, specialty groups, payers, and internal product teams to make sure analytic outputs can be trusted for clinical operations, financial performance reporting, and value-based care programs. This role sits at the intersection of healthcare data operations, quality engineering, and compliance and plays a critical part in enabling downstream analytics, actuarial evaluations, and performance benchmarking.
Requirements
What You’ll Do
- Data Quality Engineering: Develop, maintain, and automate data validation checks across claims, eligibility, EMR, and care management data.
- Reconciliation & Accuracy: Reconcile payer rosters, attribution files, and eligibility against claims and EMR data to ensure patient and cohort accuracy.
- Standards & Rule Definitions: Define quality rules for accuracy, completeness, timeliness, conformity, and uniqueness—mapped to business logic and regulatory requirements.
- Root Cause Analysis: Investigate data anomalies, ingestion failures, missingness patterns, mismatched identifiers, and lineage gaps; document root cause and corrective actions.
- Metadata & Lineage: Maintain source-to-target mapping, lineage documentation, and metric definition dictionaries with data engineering and analytics teams.
- Contract & Program Support: Ensure data meets quality thresholds for actuarial and contract performance use cases such as shared savings reconciliation and risk measurement.
- Provider & Payer Partnership: Collaborate with providers, payers, and vendors on file specifications, corrections, protocols, and reconciliation cadence.
- Data Onboarding & Integration: QA new data feeds (claims, eligibility, EMR extracts) to confirm conformity to specifications and downstream readiness.
- Governance & Controls: Contribute to data governance frameworks, access controls, and audit trails aligned with HIPAA, HITRUST, and internal standards.
Minimum Qualifications
- 3+ years in healthcare data quality, healthcare data operations, data engineering, or health informatics.
- Experience validating claims/eligibility (837/835 structures, CPT/HCPCS/ICD-10), NPI/taxonomy, and payer/provider data.
- SQL proficiency (joins, window functions, CTEs) and comfort with large relational datasets.
- Experience reconciling payer attribution and roster data with eligibility and claims.
- Understanding of value-based care concepts (attribution, TCOC, PMPM, risk adjustment, quality measures).
- Strong problem decomposition, documentation, and cross-team communication skills.
Preferred Qualifications
- Experience with HEDIS/STARS, RAF/HCC models, or other healthcare measurement frameworks.
- Familiarity with HL7/FHIR, CCD/CCDA, or EMR integration patterns.
- Python (pandas) or R for data validation scripting.
- Exposure to ETL/ELT tooling (dbt, ADF, SSIS, Informatica, etc.).
- Cloud data platforms (Azure Synapse/SQL, Databricks, Snowflake, Redshift).
- Experience contributing to or operating within data governance frameworks.
Tech Stack You May Use Here
- Data: SQL Server/Azure SQL/Synapse, Databricks, Snowflake, APIs/Flat Files
- Validation: SQL, Python (pandas), dbt (testing), Great Expectations (optional)
- BI (awareness): Power BI, Tableau
- DevOps: Git, CI/CD for data artifacts, lineage tools
- Standards: HIPAA, HITRUST, payer/provider file specifications
