Data Quality Engineer (Contract)

Wellnecity • San Juan, PR

4d • $40 - $60 • Hybrid

About The Position

Wellnecity is seeking up to two Data Quality Engineers (independent contractors) to support a large-scale data migration initiative, followed by ongoing data quality operations across healthcare datasets (claims, eligibility, and enrollment). These consultants will focus on validating, reconciling, and improving data quality across systems, ensuring accuracy and usability for downstream analytics and client delivery. The consultant will be energized by collaborating with complex, imperfect data and contributing to the continuous improvement of Wellnecity’s data infrastructure and product capabilities. This consultancy is offered on a contract basis, with an expected initial term of up to one year.

Requirements

• 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.

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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level