Data Engineer - Governance and QA

Data Engineer - Governance and QA

The Healthcare Data Reporting team delivers high‑quality outbound files and data feeds to clients and exchange partners across claims, utilization, accumulator, and other enterprise health data domains. We are seeking a Data Engineer with deep experience in data governance and QA automation to take ownership of data quality across our reporting pipelines and configuration-based reporting suite.

This role ensures the accuracy, stability, and trustworthiness of our outbound data products by establishing automated data quality frameworks, improving reporting pipelines, and governing data contracts. While a small part of the role involves hands-on manual QA, the majority focuses on building scalable systems, frameworks, and automated testing strategies that ensure our data is trusted, secure, and production-ready.

This is a highly technical role ideal for someone who thrives at the intersection of data engineering, software QA, automation, and quality governance.

 

Location: Hybrid - Dallas, Texas highly preferred. Hybrid schedule is expected to be in office 3x a week at minimum. 

 

Responsibilities:

Data Quality & Validation

• Own end‑to‑end data quality, integrity, and reliability across staging, transformation, and outbound reporting layers.
• Ensure deterministic logic, repeatability, and consistent outcomes across reporting pipelines and configuration‑driven reporting assets.
• Implement automated data quality checks using Python‑based frameworks (dbt tests, Pytest, Soda, Great Expectations, or similar).
• Enforce data contracts and validation rules for all outbound files and client deliverables.

 

Test Strategy & Automation

• Define and execute the overall test strategy for outbound reporting, including unit, integration, regression, and end‑to‑end testing.
• Build and maintain automated test suites to validate field mappings, transformation logic, and reporting configurations.
• Integrate automated QA processes into CI/CD pipelines in partnership with Platform Engineering.
• Ensure all pipelines and data products are testable, observable, and instrumented for automated quality checks.

 

Cross-functional Collaboration

• Partner with Data Engineering, Platform, and centralized QA teams to align on testing standards, frameworks, and best practices.
• Provide subject matter expertise on data quality, pipeline testing, and reporting logic across the enterprise.
• Influence architectural decisions related to data models, reporting pipelines, and configuration‑driven report generation.

 

Quality Governance & Standards

• Establish and maintain clear QA documentation, including test plans, cases, validation rules, and data quality SLAs.
• Implement version control, automated validation scripts, and monitoring dashboards to support scalable quality governance.
• Contribute to continuous improvement of data governance, quality controls, and reliability engineering practices.

 

Targeted Manual Validation (As Needed)

• Perform manual QA for new report configurations, schema changes, mapping logic, and first‑time outbound file launches where automation is insufficient.
• Validate SQL transformations, metadata, and schema consistency across reporting assets.
• Document defects, track resolution, and lead root‑cause analysis for data quality issues.

 

Required Qualifications:

• Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or related field — or equivalent experience.
• 5+ years of experience in QA Engineering, Data Engineering, or Data Quality within data‑intensive or regulated environments (healthcare preferred).
• Python experience for automated testing, data validation, and quality frameworks.
• Hands‑on experience with automated data quality/testing tools (dbt tests, Pytest, Soda, Great Expectations, or similar).
• Experience working within CI/CD environments (GitHub Actions, GitLab, Jenkins, etc.).
• Strong understanding of data modeling and data architecture concepts (dimensional, normalized, and reporting models).
• Excellent analytical, troubleshooting, and root‑cause analysis skills.
• Clear communication skills with the ability to translate technical findings into business context.
• High attention to detail with a strong sense of ownership for data accuracy and reliability.

 

Preferred Qualifications:

• Experience with healthcare datasets (claims, eligibility, utilization, accumulators).
• Familiarity with cloud platforms (AWS, Azure, or GCP) and modern data platform components.
• Experience with Databricks for data processing, testing, and pipeline automation.
• Experience with data quality SLAs, observability tooling, or data reliability engineering.
• Background in configuration‑driven reporting or client‑specific outbound file generation.

 

Benefits: