REQUIREMENTS - 8+ years of software engineering experience with a 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 that support customer-facing applications- Working knowledge of cloud infrastructure (e.g., 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: you care about how customers use what you build- Preferred: full-stack experience, or experience building web UIs with a modern framework (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 COMPENSATION This role pays $185,000 to $230,000 per year, based on experience, in addition to equity in the form of Restricted Stock Units (RSU). You will:- Design, build and operate large-scale data pipelines for ingesting, transforming, matching and syncing client data, using PySpark, Scala and SQL- Build and improve the entity matching service that resolves client records against H1's core data layer, including its underlying schema- Develop data asset management features such as scoring, status assignment and configurable intake- Build the APIs and backend services that power customer-facing UIs and internal tools, making pipeline outputs easy to explore, act on and trust- Contribute to customer-facing and internal applications that help bring new products to market- Build self-service internal tools that let operations, customer success and other non-technical teams work with data without engineering help- Work directly with messy enterprise client data, debugging issues end to end and making ingestion more reliable and repeatable- Integrate clinical and provider data as new products and customers come onto the platform- Work with product and design partners to shape features from customer problem through shipped product- Improve pipeline performance, reliability, observability, testing and documentation- Contribute to architecture decisions and design and code reviews- Collaborate with adjacent teams that own feed delivery, access infrastructure, public-facing APIs and roster processing.