Data Engineer – Governance, QA

Role Overview

• 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.
• 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.
• 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.
• 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.
• 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.

Requirements

• 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.

Tech Stack

• Jenkins
• Python
• SQL

Benefits

• Medical Insurance
• Dental Insurance
• Vision Insurance
• Short & Long Term Disability
• Life Insurance
• 401k with company match
• Paid Time Off
• Paid Parental Leave