Data Engineer - Governance and QA

About the role

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.

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

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

Company Values

Lantern values LIGHT, which stands for:

• Logic: Using logic in decision-making and understanding that progress is critical to making change.
• Inclusion: Treating others with respect and valuing diversity and inclusion.
• Grit: Having the drive, ambition, and resilience to tackle big problems.
• Humility: Caring deeply for our customers and keeping humanity in all decisions.
• Integrity: Guided by truth and authenticity.
• Teamwork: Thriving in a collaborative environment.

Application

Please apply to our role & someone from our Talent Acquisition Team will reach out to help you navigate our interview process.