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Data Engineer - Governance and QA
- Dallas, Texas, United States
- Dallas, Texas, United States
Über
About You
You use LOGIC in your decision making and understand that progress is critical to making change. You focus on the execution of your content while balancing a fast‑paced environment and you take the time to celebrate both the small & big wins.
INCLUSION
is a core tenant of your personal beliefs. A diverse and inclusive environment is incredibly important to you. You understand and desire to be a part of a diverse team with different experiences and perspectives & you cherish the differences in each individual that you interact with.
You have the GRIT, drive and ambition to tackle big problems. Big problems require big ideas and a team that supports new ideas.
You care deeply for your customers and are driven to keep HUMANITY in all decisions. Your customers aren’t just the individuals using your product. They are the driving factor in your motivation to make a change.
Integrity guides you in life. Focusing on the TRUTH vs. giving people the answers they want to hear.
You thrive in a Team Environment. Collaboration is key in innovation and creating change.
These pillars of
LIGHT
are a reminder to our team that we are making a difference by providing guidance and support in navigating the often complex and confusing landscape of healthcare. We hope that through this
LIGHT , individuals can find their way to the best care, resources, and support they need to get back to life.
If this sounds like you, we would love to connect to speak further about career opportunities at Lantern.
Please apply to our role & someone from our Talent Acquisition Team will reach out to help you navigate our interview process.
Data Engineer - Governance and QA 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
Medical Insurance
Dental Insurance
Vision Insurance
Short & Long Term Disability
Life Insurance
401k with company match
Paid Time Off
Paid Parental Leave
Lantern does not discriminate on the basis of race, sex, color, religion, age, national origin, marital status, disability, veteran status, genetic information, sexual orientation, gender identity or any other reason prohibited by law in provision of employment opportunities and benefits.
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Sprachkenntnisse
- English
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