About the RoleDefinitive Healthcare is seeking a Data Engineer to build, maintain, and support reliable data pipelines and transformations that power critical healthcare data products and customer-facing solutions.
This role is accountable for the quality, reliability, and timely delivery of assigned pipeline components and data changes. Working with engineering and domain experts, the Data Engineer develops moderate-complexity solutions, troubleshoots pipeline and data quality issues, and supports production operations across Claims and Consumer data domains.
Success in this role requires hands-on experience with Python, SQL, Spark/PySpark, cloud data platforms, workflow orchestration, and modern engineering practices. The Data Engineer works with growing independence, follows established standards, communicates progress and blockers, and collaborates with engineers, analysts, Product, Support, and data consumers.
What You'll Do Pipeline Engineering & Component Ownership - Build, maintain, test, and support moderate-complexity ETL/ELT pipelines, transformations, and data components using Python, SQL, Spark/PySpark, and related technologies.
- Own defined pipeline components from implementation through testing, deployment, documentation, and production support with technical guidance as needed.
- Troubleshoot pipeline failures, data quality issues, and performance problems; identify root causes with support and implement practical, maintainable fixes.
Design, Quality & Operational Reliability - Contribute to component-level technical designs, estimates, and implementation plans while following established architecture and engineering standards.
- Apply team practices for data modeling, testing, code review, observability, security, source control, CI/CD, and operational support.
- Monitor pipeline performance and reliability, surface risks and blockers early, and identify opportunities to improve quality, efficiency, and maintainability.
Domain & Stakeholder Partnership - Develop working knowledge of Claims and Consumer data structures, source-to-target transformations, pipeline dependencies, and expected data quality standards.
- Collaborate with engineers, QA, analysts, Product, Support, and data consumers to clarify requirements, resolve issues, and deliver reliable data changes.
- Assess downstream impacts of data and schema changes, communicate technical status clearly, and support vendor-delivered data sources and customer issues.
Collaboration, Growth & Engineering Practices - Participate actively in code reviews, pairing, sprint activities, technical discussions, documentation, and team knowledge sharing.
- Support peers and less-experienced engineers by sharing knowledge, providing constructive review feedback, and modeling effective engineering practices.
- Use approved AI-assisted development tools responsibly to improve productivity, documentation, test coverage, and code quality while following team review standards.
What You'll Bring - Bachelor's degree in Computer Science, Engineering, Information Systems, or equivalent practical experience.
- 2+ years of Data Engineering, Software Engineering, or related experience building and supporting production data solutions.
- Proficiency in Python and SQL, with hands-on experience developing and maintaining production data pipelines.
- Experience with Apache Spark/PySpark or equivalent distributed data processing technologies.
- Experience with cloud platforms such as AWS, Azure, or GCP and modern data platforms such as Databricks, Snowflake, Delta Lake, or equivalent technologies.
- Experience with workflow orchestration technologies such as Airflow, Argo, or equivalent platforms.
- Working knowledge of relational databases, data modeling, testing, code review, observability, source control, CI/CD, and production support practices.
- Ability to own defined technical work, troubleshoot issues, collaborate effectively, and communicate progress, risks, and blockers.
Preferred Qualifications- Healthcare data experience, including claims, pharmacy, eligibility, consumer, provider, or reference data.
- Experience processing large datasets using lakehouse or medallion architecture patterns.
- Experience deploying, operating, or supporting Spark workloads in Kubernetes or similar containerized environments.
- Experience supporting enterprise data products, analytics platforms, or business-critical data services in regulated environments.
- Experience onboarding or supporting vendor-delivered datasets and evaluating schema or downstream consumer impacts.
- Experience using AI-assisted engineering tools to improve development productivity, testing, documentation, and code quality.
Compensation and BenefitsThe salary range for this position is $93,000 - $141,000 per year, which represents the base pay the company reasonably and in good faith expects to pay for this role. Actual pay within this range will be determined based on factors such as relevant experience, skills, and qualifications.
Depending on the position, employees may also be eligible to participate in a company bonus or commission plan. All employees are eligible for a comprehensive benefits package, including medical, dental, and vision coverage, unlimited paid time off, and participation in the company's 401(k) plan with employer contribution.
