The ad says“Strong understanding of software quality practices: testing, code review, documentation, and CI/CD”
The ad says“Build the data quality checks, validation logic, and reconciliation that let non-engineers trust pipeline output without reading the code”
The ad says“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”
The ad says“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”
The ad says“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”
The ad says“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”
Values
The ad says“Familiarity with AWS services (S3, Lambda, ECS, or similar) a plus”
The ad says“Familiarity with clinical trial data, healthcare data, or another regulated data domain a plus”
The ad says“Familiarity with entity matching or data mastering a plus”
The ad says“Familiarity with entity matching or data mastering a plus”
The ad says“Familiarity with clinical trial data, healthcare data, or another regulated data domain a plus”
The ad says“Experience with pipeline orchestration tooling (Argo, Airflow, Databricks, dbt, or similar) preferred”
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
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
Benefits
Flexible work hours
Commuter benefits
Stock options
Computer setup
Work from home opportunities
Health & life insurance
Retirement options
Unlimited PTO
Flex Give & Flex Spend
Impactful BRGs
Preferred experience
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
Working in an operationally-driven environment where reliability and on-time delivery matter as much as new feature work
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
Working with PySpark or a comparable distributed processing framework on datasets too large for a single machine
Building and shipping production data pipelines in Python, with an understanding of what makes them reliable and maintainable under real load
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
Writing SQL well enough to answer hard questions about data, not just retrieve it
Familiarity with clinical trial data, healthcare data, or another regulated data domain a plus
Experience working with cross-functional and non-technical stakeholders
Familiarity with entity matching or data mastering a plus
Demonstrated experience building and maintaining production-grade data pipelines in Python
Hands-on experience with PySpark or a similar distributed data processing framework
3+ years of experience in software or data engineering, with meaningful Python in your background
Familiarity with AWS services (S3, Lambda, ECS, or similar) a plus
Experience with pipeline orchestration tooling (Argo, Airflow, Databricks, dbt, or similar) preferred
Strong SQL skills, including working with large, messy, multi-source datasets
Strong understanding of software quality practices: testing, code review, documentation, and CI/CD
