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.
As a Data Quality Engineer, you’ll play a key role in a large-scale healthcare data migration and ongoing data quality program.
You’ll work across claims, eligibility, and enrollment datasets sourced from dozens of systems and client feeds.
Your work will ensure data is accurate, complete, consistent, and reliable for analytics and client-facing solutions.
You’ll combine advanced SQL and Python with strong analytical thinking to validate pipelines, mappings, and transformations.
The role involves investigating complex, imperfect datasets and partnering closely with engineering and product teams to resolve discrepancies.
You’ll also help establish repeatable quality standards, documentation, and monitoring practices that strengthen the broader data infrastructure.
This is a remote, contract opportunity suited to a hands-on data professional who enjoys solving challenging data problems in a fast-paced environment.
Accountabilities
- 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.
Requirements
- 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.
Benefits
- 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.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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