Knit Health
Data Engineer II
Requirements
Candidates should have 4-6 years of professional data engineering experience with strong proficiency in Python, SQL, and large-scale data processing frameworks like Spark. A solid understanding of cloud infrastructure and data modeling principles is required, with preference given to those with experience in healthcare data standards.
Responsibilities
You will design, build, and maintain scalable data pipelines and infrastructure to support a clinical AI platform. Additionally, you will collaborate with science and product teams to integrate healthcare data sources and establish standards for data quality and observability.
About the role
About Knit Health Knit Health is building a novel clinical foundation model to improve how healthcare is delivered. We combine expertise in AI with deep clinical knowledge to develop safe, trustworthy systems that improve care, expand access, and reduce waste. Knit is led by a founding team with roots at the University of California, Berkeley. The team has developed a novel AI architecture designed to reason like physicians. We are now using that foundation model, together with frontier clinical LLMs, to build a next-generation clinical intelligence platform for healthcare providers. We are venture backed and work with U.S.-based health systems and data providers. What you'll do 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. Minimum qualifications 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. Nice-to-haves 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. Location San Francisco, CA preferred, with a hybrid work arrangement of two to three days per week in the office. Remote candidates may be considered based on experience and qualifications. Salary Range Knit Health offers a competitive compensation package that includes base salary, equity, and opportunities for advancement. The starting salary range is approximately $150,000 to $175,000 per year. Benefits Generous benefits for full-time employees include: medical, dental, and vision coverage with 100% of premiums paid for employees and dependents (full coverage for dental, vision, and our Gold medical plan; employees may choose to buy up to Platinum); coverage begins on the first day of employment. Additional benefits include a 401(k) plan and 24 days of PTO annually. Final Notes Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities, and activities may change at any time with or without notice. Knit Health is an equal opportunity employer and is committed to a diverse workplace. People from diverse racial, ethnic and cultural backgrounds, women, LGBTQ+ individuals, and persons with disabilities are highly encouraged to apply.