Job description

At H1, we believe access to the best healthcare information is a basic human right. Our mission is to provide a platform that can optimally inform every doctor interaction globally. This promotes health equity and builds needed trust in healthcare systems. To accomplish this, our teams harness the power of data and AI-technology to unlock groundbreaking medical insights and convert those insights into action that result in optimal patient outcomes and accelerates an equitable and inclusive drug development lifecycle. Visit h1.com to learn more about us. As part of H1’s hiring process, all candidates are required to participate in an in-person final interview. Depending on your location, this may require travel. H1's Data Network (H1DN) team is the client-data mastering network at the core of how H1's products get their data. We run production ingestion for major enterprise customers. Clinical trial data is one of our highest-visibility streams: it feeds decisions about where trials run and who runs them, and the people who depend on it are as often clinical experts as they are engineers. SLAs and customer expectations drive how we work, and we're looking for engineers who are energized by that. WHAT YOU'LL DO AT H1 As a Data Engineer II on the H1DN team, you will build and operate the pipelines behind H1's clinical trials data. You'll work primarily in Python, PySpark, and SQL, and you'll work directly with clinical subject matter experts and Customer Success Managers to turn their domain knowledge into pipeline logic that holds up in production. You will: - Build and maintain the Python and PySpark pipelines behind the CTMS trial data pipeline intake workflows, including scoring and status logic. - Develop the transformation logic that maps raw trial and customer data to H1's internal data models, handling diverse source formats including CSV, JSON, Parquet, and APIs. - Write and tune SQL against large datasets to investigate data questions, validate pipeline output, and support analysis that clinical SMEs and customer-facing teams depend on. - Turn around customer-driven changes quickly, scoping requests as they arrive, shipping changes that hold up under enterprise SLAs, and reworking logic as customer needs shift mid-flight. - Partner with clinical SMEs to translate domain expertise into concrete data rules, then walk them through the results, explain what the pipeline did and why, and fold their feedback back into the logic. - Build the data quality checks, validation logic, and reconciliation that let non-engineers trust pipeline output without reading the code. - Participate in code reviews, maintaining a high bar for quality and adherence to engineering standards. - Monitor and improve pipeline observability, contributing to alerting and dashboards that surface job health and data anomalies for both the team and internal users. ABOUT YOU You are a data engineer with a strong Python foundation and real distributed-processing experience. You're drawn to high-impact teams where the work is tangible: pipelines running, enterprise customers getting their data on time, clinical data that people make real decisions from. You're comfortable in an environment where recurring production runs and customer SLAs shape day-to-day priorities, and where a customer request can reorder your week. You'd rather sit down with a domain expert and understand why the data looks the way it does than build to a spec handed to you secondhand. You bring experience: - Building and shipping production data pipelines in Python, with an understanding of what makes them reliable and maintainable under real load - Working with PySpark or a comparable distributed processing framework on datasets too large for a single machine - Writing SQL well enough to answer hard questions about data, not just retrieve it - Working in an operationally-driven environment where reliability and on-time delivery matter as much as new feature work - Working directly with non-engineering partners, subject matter experts, analysts, or customer-facing teams, and communicating clearly about data with people who don't read code - Holding a high bar in code review and expecting the same from those who review your work - Identifying data problems early and seeing work through to resolution rather than handing it off REQUIREMENTS - 3+ years of experience in software or data engineering, with meaningful Python in your background - Demonstrated experience building and maintaining production-grade data pipelines in Python - Hands-on experience with PySpark or a similar distributed data processing framework - Strong SQL skills, including working with large, messy, multi-source datasets - Strong understanding of software quality practices: testing, code review, documentation, and CI/CD - Experience working with cross-functional and non-technical stakeholders - Experience with pipeline orchestration tooling (Argo, Airflow, Databricks, dbt, or similar) preferred - Familiarity with clinical trial data, healthcare data, or another regulated data domain a plus - Familiarity with entity matching or data mastering a plus - Familiarity with AWS services (S3, Lambda, ECS, or similar) a plus COMPENSATION This role pays $110,000 to $135,000 per year, based on experience, in addition to stock options. Anticipated role close date: 10/20/2026 H1 OFFERS - Full suite of health insurance options, in addition to generous paid time off - Pre-planned company-wide wellness holidays - Retirement options - Health & charitable donation stipends - Impactful Business Resource Groups - The opportunity to work with leading biotech and life sciences companies in an innovative industry with a mission to improve healthcare around the globe H1 is proud to be an equal opportunity employer that celebrates diversity and is committed to creating an inclusive workplace with equal opportunity for all applicants and teammates. Our goal is to recruit the most talented people from a diverse candidate pool regardless of race, color, ancestry, national origin, religion, disability, sex (including pregnancy), age, gender, gender identity, sexual orientation, marital status, veteran status, or any other characteristic protected by law. H1 is committed to working with and providing access and reasonable accommodation to applicants with mental and/or physical disabilities. If you require an accommodation, please reach out to your recruiter once you've begun the interview process. All requests for accommodations are treated discreetly and confidentially, as practical and permitted by law.

Requirements

Must-haves

As part of H1’s hiring process, all candidates are required to participate in an in-person final interview. Depending on your location, this may require travel.
Build and maintain the Python and PySpark pipelines behind the CTMS trial data pipeline intake workflows, including scoring and status logic.
Develop the transformation logic that maps raw trial and customer data to H1's internal data models, handling diverse source formats including CSV, JSON, Parquet, and APIs.
Write and tune SQL against large datasets to investigate data questions, validate pipeline output, and support analysis that clinical SMEs and customer-facing teams depend on.
Turn around customer-driven changes quickly, scoping requests as they arrive, shipping changes that hold up under enterprise SLAs, and reworking logic as customer needs shift mid-flight.
Partner with clinical SMEs to translate domain expertise into concrete data rules, then walk them through the results, explain what the pipeline did and why, and fold their feedback back into the logic.
Build the data quality checks, validation logic, and reconciliation that let non-engineers trust pipeline output without reading the code.
Participate in code reviews, maintaining a high bar for quality and adherence to engineering standards.
Monitor and improve pipeline observability, contributing to alerting and dashboards that surface job health and data anomalies for both the team and internal users.
Building and shipping production data pipelines in Python, with an understanding of what makes them reliable and maintainable under real load
Working with PySpark or a comparable distributed processing framework on datasets too large for a single machine
Writing SQL well enough to answer hard questions about data, not just retrieve it
Working in an operationally-driven environment where reliability and on-time delivery matter as much as new feature work
Working directly with non-engineering partners, subject matter experts, analysts, or customer-facing teams, and communicating clearly about data with people who don't read code
Holding a high bar in code review and expecting the same from those who review your work
Identifying data problems early and seeing work through to resolution rather than handing it off
3+ years of experience in software or data engineering, with meaningful Python in your background
Demonstrated experience building and maintaining production-grade data pipelines in Python
Hands-on experience with PySpark or a similar distributed data processing framework
Strong SQL skills, including working with large, messy, multi-source datasets

Nice-to-haves

Experience with pipeline orchestration tooling (Argo, Airflow, Databricks, dbt, or similar) preferred
Familiarity with clinical trial data, healthcare data, or another regulated data domain a plus
Familiarity with entity matching or data mastering a plus
Familiarity with AWS services (S3, Lambda, ECS, or similar) a plus

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aipythonsqlci/cdaws

Platform: lever
Effort: quick

Benefits

Benefits detected in the listing.

Health Insurance

Paid Time Off

Wellness