Data Engineer

Definitive Healthcare is seeking a Data Engineer to build, maintain, and support reliable data pipelines and transformations that power healthcare data products and customer-facing solutions. The role owns assigned pipeline components, troubleshoots data and reliability issues, supports production operations across Claims and Consumer data domains, and collaborates with engineering and business stakeholders.

Responsibilities:

• 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
• 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
• 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
• 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

Requirements:

• 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
• 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