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Salary: USD 185,000 - 230,000 / annual
H1 is a healthcare data and AI platform on a mission to provide optimal healthcare information globally and promote health equity. The Health Plans Software Engineering group builds products that help health plans maintain accurate provider data. The Directory Accuracy team owns the master data management and client data integration platform that ingests, matches, and standardizes client data to support provider directory accuracy, site feasibility, and healthcare professional credentialing.
As a Staff Software Engineer on the Health Plans team, you will design, build, and operate large-scale data pipelines and services that health plan and life sciences customers rely on daily. This is a hands-on role where you'll spend most of your time writing code in PySpark, Scala, and SQL, solving complex data problems at scale, and shipping features directly used by customers and internal teams. You'll join an established team of a Staff engineer, a Senior engineer, and two contractors, reporting to the Director of Software Engineering for Health Plans.
Key responsibilities include:
- Designing, building, and operating large-scale data pipelines for ingesting, transforming, matching, and syncing client data using PySpark, Scala, and SQL
- Building and improving the entity matching service that resolves client records against H1's core data layer
- Developing data asset management features such as scoring, status assignment, and configurable intake
- Building APIs and backend services powering customer-facing UIs and internal tools
- Contributing to customer-facing and internal applications that bring new products to market
- Creating self-service internal tools for operations, customer success, and non-technical teams
- Working directly with messy enterprise client data, debugging issues end-to-end, and improving ingestion reliability
- Integrating clinical and provider data as new products and customers join the platform
- Collaborating with product and design partners to shape features from problem identification through launch
- Improving pipeline performance, reliability, observability, testing, and documentation
- Contributing to architecture decisions and design and code reviews
- Collaborating with adjacent teams owning feed delivery, access infrastructure, public APIs, and roster processing
You are a senior big data engineer who loves building products, not just pipelines. You care about how data reaches end users—whether customers in the app or teammates using internal tools. You're most engaged deep in code, tuning Spark jobs, untangling messy source data, and shipping features customers notice. You take end-to-end ownership of systems and know how to improve existing codebases. You thrive in fast-moving environments and communicate clearly with engineers, product partners, and non-technical stakeholders.
REQUIREMENTS
- 8+ years of software engineering experience with strong focus on data engineering and building data-driven products
- Deep experience designing, building, and running production big data pipelines at scale (ETL/ELT, orchestration, scheduling, monitoring)
- Strong proficiency in PySpark, Scala, Python, and SQL
- Solid experience with distributed data processing and performance tuning in Spark
- Experience building APIs or backend services supporting customer-facing applications
- Working knowledge of cloud infrastructure (AWS, GCP, or Azure)
- Hands-on experience with entity resolution, record matching, or master data management
- Experience with data quality, scoring, or validation frameworks
- Comfort working directly with client data, including messy source files, and onboarding enterprise customers
- Product-engineering mindset: caring about how customers use what you build
- Preferred: full-stack experience or experience building web UIs with modern frameworks (e.g., React)
- Preferred: experience building products that turn complex data into customer-facing features
- Preferred: experience building internal self-service tools
- Preferred: experience with healthcare data such as provider or HCP directories, rosters, credentialing, clinical trial or site feasibility data
- Preferred: experience with CTMS or other clinical systems data
