Data Engineer

About Definitive Healthcare:
At Definitive Healthcare (NASDAQ: DH), we’re passionate about turning data, analytics, and expertise into meaningful intelligence that helps our customers achieve success and shape the future of healthcare. We empower them to uncover the right markets, opportunities, and people—paving the way for smarter decisions and greater impact. Headquartered just outside of Boston, Massachusetts, Definitive Healthcare operates across North America, Europe, and India, supporting a growing global client base of more than 2,400 customers since our founding in 2011.We’re

also a great place to work. In 2024 and 2025, we earned multiple workplace honors, including Built In’s 100 Best Places to Work in Boston (both years), a Stevie Bronze Award for Great Employers, and recognition as a Great Place to Work in India.

We foster a collaborative, inclusive culture where diverse perspectives drive innovation. Through programs like Definitive Cares and our employee-led affinity groups we strive to promote connection, education, and inclusion. About the Role Definitive 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.

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