> Markdown version of [/jobs/ext/4022879-data-engineer-ii](https://www.wearedevelopers.com/jobs/ext/4022879-data-engineer-ii). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer II - **Company:** Knit Health, Inc. - **Location:** San Francisco, CA, United States - **Experience:** Experienced - **Salary:** $150,000.0 - $175,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Big Data, Cloud Computing, Software Quality, Computer Programming, Databases, Data Architecture, Information Engineering, Extract Transform Load (ETL), Data Transformation, Data Systems, Database Queries, Distributed Systems, Interoperability, Python (Programming Language), Machine Learning, Operational Databases, Standard Sql, Data Processing, Cloud Platform System, EHR Systems, Fast Healthcare Interoperability Resources, Apache Spark, AI Platforms, Information Technology, Code Testing, Health Level Seven International, Databricks - **Published:** October 30, 2026 - **Apply:** https://startup.jobs/data-engineer-ii-knit-health-10623068 ## About the Role We're looking for candidates with strong engineering fundamentals, experience building production data systems, and the ability to independently solve complex technical problems. * Systems thinking and problem-solving: Ability to reason from first principles, understand how complex systems work, and independently design and troubleshoot solutions without relying exclusively on off-the-shelf tools. * Data engineering fundamentals: Strong understanding of data modeling, ETL/ELT, orchestration, database architecture, and data quality, including the underlying principles that make data systems scalable and reliable. * Large scale data processing: Hands-on experience processing large datasets using Spark or comparable frameworks, with an understanding of distributed computing, performance, and scalability. * Engineering experience: Approximately 4-6 years of professional data engineering experience, or equivalent hands-on experience designing, building, and maintaining production data pipelines and infrastructure. * Programming proficiency: Strong Python and SQL skills, including complex queries, data transformations, and writing clean, maintainable, well-tested code. * Cloud infrastructure: Hands-on experience with a major cloud platform such as Azure, AWS, or GCP. We value demonstrated engineering capability over a specific educational path. A background in computer science, engineering, mathematics, physics, or a related technical discipline is valuable, but we also welcome candidates who have developed equivalent expertise through hands-on experience., * Experience working with healthcare data, particularly provider-side clinical and claims workflows, including submitted and adjudicated claims. * Experience with Epic, EHR systems, or healthcare interoperability standards such as FHIR and HL7. * Familiarity with healthcare privacy and compliance frameworks, including HIPAA. * Experience using AI-assisted coding tools while maintaining strong code quality and testing practices. * Experience optimizing large-scale data processing workloads for cost and performance. * Experience with Azure and Databricks. ## Description As a Data Engineer II at Knit Health, you'll be a foundational member of a small, high-impact team building the data infrastructure behind our clinical AI platform. You'll help develop the clinical world data model that powers our AI systems, transforming complex healthcare data into reliable inputs for model training, research, and real-world applications. * Design, build, and maintain data pipelines and infrastructure at scale, collaborating closely with our science, AI, and product teams. * Expand our data integrations to new clinical and healthcare data sources, including the processing, modeling, and quality safeguards needed for reliable downstream use. * Build and optimize large-scale, data processing systems for performance, scalability, and reliability. * Drive data and product integrations with healthcare clients deploying our AI models. * Help shape our data architecture and establish standards for data quality, testing, documentation, and observability.