Note: The job is a remote job and is open to candidates in USA. Wellnecity is a platform focused on improving health plan performance, healthcare data management, and member outcomes. The Data Quality Engineer will support a large-scale healthcare data migration and ongoing data quality operations by validating, reconciling, and improving claims, eligibility, and enrollment data across systems. The role will develop SQL- and Python-based checks, identify discrepancies, document validation criteria, and support data quality monitoring.
Responsibilities
- Lead data validation and reconciliation efforts for large-scale data migration spanning medical claims, pharmacy claims, and eligibility and enrollment datasets – ensuring accuracy, completeness, and consistency across more than 70 data sources and hundreds of client feeds
- Validate automated field mappings against legacy data warehouse definitions, surfacing schema differences, value-domain mismatches, and transformation gaps, and partnering with Data Engineering to drive resolution
- Execute end-to-end ingestion testing on the new data ingestion platform – running test files, reconciling outputs against legacy baselines and confirming that record counts, field-level distributions, and key business metrics fall within established quality thresholds prior to production cutover
- Design and develop SQL- and Python-based data quality checks, including source-to-target reconciliation queries, distribution comparisons, completeness tests, and parity validation for templating mapping deployments applied across multiple client data feeds
- Identify, document, and triage data discrepancies – including missing records, value mismatches, schema drift, and downstream transformation errors – driving structured resolution across Data Engineering, Product, and platform teams
- Establish and maintain data quality rules and validation thresholds per data source – including field-completeness rates, record-count tolerance, value-distribution expectations, and parity criteria for templated client deployments – to ensure consistent and repeatable validation across the full migration scope
- Document validation logic, sign-off criteria, and known data caveats per source in migration tracking artifacts and run-book documentation, enabling defensible cutover decisions and providing an audit trail for completed migration phases
- Support the transition from migration to steady-state operations by operationalizing data quality checks and monitoring processes, ensuring continuity of validation rigor as each source completes cutover
- Contribute to ongoing data quality management, including identifying patterns, improving validation of workflows, and reducing recurring data issues
Skills
- • Bachelor's degree with 3–5 years of experience in data quality engineering, data operations, or analytics (Master's may substitute for experience)
- • Direct experience working with healthcare data (eligibility, enrollment, medical and pharmacy claims)
- • Advanced SQL proficiency,including writing and optimizing complex queries and experience in design, implementations, and optimization in relational SQL databases
- • Strong Python skills with experience building analytical or data validation workflows
- • Experience with data cleansing, curation, mining, manipulation, and analysis from disparate systems (SQL, Python preferred)
- • Demonstrated experience supporting large-scale data migrations, including source-to-target validation and data reconciliation
- • Experience validating data pipelines and ETL transformation logic – confirming accuracy of field mappings, derived fields, and aggregated business metrics against expected outputs
- • Proven ability to analyze complex, imperfect datasets, identify root causes, and resolve data issues
- • Experience owning or contributing to data quality processes, including defining validation logic and maintaining data integrity
- • Strong collaboration skills with experience working cross-functionally with data engineering, product, or analytics teams
Benefits
- Remote work in the United States
- Work with the latest technologies in cloud computing, data engineering, and healthcare data processing
- Shape and grow a high-quality and passionate team
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