Data Quality Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Quality Engineer based in the United States.

• Lead data validation and reconciliation activities for large-scale migration initiatives involving medical claims, pharmacy claims, eligibility, and enrollment data across 70+ sources and hundreds of client feeds.
• Validate automated field mappings against legacy warehouse definitions, identifying schema differences, value-domain mismatches, transformation gaps, and other inconsistencies.
• Partner with Data Engineering and other technical teams to investigate discrepancies and drive issues through to resolution.
• Perform end-to-end ingestion testing by executing test files, comparing results with legacy baselines, and validating record counts, field distributions, and key business metrics before production cutover.
• Design and maintain SQL- and Python-based data quality checks, including source-to-target reconciliation, completeness testing, distribution analysis, and parity validation.
• Identify, document, and triage missing records, value mismatches, schema drift, transformation errors, and other data integrity issues.
• Establish data quality rules and validation thresholds for individual data sources, including completeness rates, record-count tolerances, expected value distributions, and deployment parity criteria.
• Maintain migration documentation, validation logic, sign-off criteria, known data caveats, runbooks, and other artifacts that support defensible cutover decisions and auditability.
• Help transition validated data sources from migration activities into steady-state data quality operations and monitoring.
• Identify recurring data-quality patterns and recommend improvements to validation workflows and processes.
• Collaborate closely with Data Operations, Data Engineering, Product, and analytics stakeholders to continuously improve data reliability and usability.

• Bachelor’s degree with 3–5 years of experience in data quality engineering, data operations, analytics, or a related field; a Master’s degree may substitute for professional experience.
• Direct experience working with healthcare datasets, particularly eligibility, enrollment, medical claims, and pharmacy claims.
• Advanced SQL expertise, including developing, optimizing, and implementing complex queries within relational databases.
• Strong Python programming skills with experience developing analytical and data-validation workflows.
• Experience cleansing, curating, mining, manipulating, and analyzing data from disparate systems.
• Demonstrated experience supporting large-scale data migrations, including source-to-target validation and reconciliation.
• Hands-on experience validating data pipelines and ETL transformations, including field mappings, derived fields, and aggregated business metrics.
• Strong analytical and problem-solving abilities, with the capacity to investigate imperfect datasets, identify root causes, and resolve complex data issues.
• Experience establishing or contributing to data quality processes, validation rules, and data integrity standards.
• Strong attention to detail and a methodical approach to testing, documentation, and quality assurance.
• Excellent collaboration and communication skills, with experience working cross-functionally with engineering, product, analytics, and operations teams.
• Ability to work independently and effectively in a fast-paced, entrepreneurial remote environment.
• Willingness to complete a practical SQL and Python assessment using representative healthcare data as part of the selection process.

• Estimated compensation of $40–$60 per hour.
• Remote contract opportunity available to professionals located in the United States.
• Initial contract term of up to one year.
• Opportunity to contribute to a large-scale healthcare data migration with direct impact on data quality and downstream analytics.
• Exposure to modern cloud computing, data engineering, and healthcare data-processing technologies.
• Close collaboration with data operations, engineering, product, and analytics professionals.
• Opportunity to help establish scalable data quality standards, workflows, and operational practices.
• Chance to contribute to improvements in healthcare data infrastructure and the delivery of higher-quality health plan solutions.
• Expected application window closes October 23, 2026, subject to change.