Knit Health
Data Engineer II
San Francisco · Mid · Full-time
Sponsorship not specified$110k-$135kDetected 37 days ago
PythonCode ReviewGitSQLDatabricksAWSGCPAzureCloud PlatformsMachine LearningData EngineeringLLMsResearchCommunication
About the role
- Knit is led by a founding team from the University of California Berkeley who have developed a novel AI architecture which learns to reason like physicians.
- The starting salary range for the Data Engineer II is approximately $110,000 to $135,000 per year.
- Duties, responsibilities, and activities may change at any time with or without notice.
Responsibilities
- As a Data Engineer II, you'll be a foundational member of a small, high-impact team building the data backbone of our clinical AI platform. Your work will directly enable the research, products, and decisions that shape where the company goes next.
- Design, build, and maintain the data pipelines and infrastructure that power both our product and research applications - from ingestion through analytics-ready delivery
- Partner closely with our data science and ML teams to integrate, structure, and scale the stack as our needs evolve
- 2-5 years of professional experience specifically in data engineering (building data pipelines, ETL/ELT workflows, data modeling, and warehouse architectures)
- Engineering fundamentals: comfort with version control (Git), code review, testing, and the habits of writing code others can read, maintain, and trust
- As a Data Engineer II, you'll be a foundational member of a small, high-impact team building the data backbone of our clinical AI platform.
- comfort with version control (Git), code review, testing, and the habits of writing code others can read, maintain, and trust
- We combine expertise in AI with deep clinical knowledge to develop safe, trustworthy systems that improve care, expand access, and reduce waste.
- We're now closing the loop and using our novel foundation model, together with frontier clinical LLMs, to build a next generation clinical intelligence platform for providers.
- Own meaningful pieces of our data platform end-to-end, from design through production
Requirements
- An advanced degree (MS or PhD) in data science, computer science, computer engineering, or an adjacent technical discipline, paired with demonstrable data engineering project work
- Regardless of path, you should be able to demonstrate proficiency in SQL and Python and hands-on experience with at least one major cloud platform (Azure, AWS, etc.).
- Communication: the ability to explain technical tradeoffs clearly, in writing and in conversation, to both engineers and non-engineers
- 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.
Nice to have
- hands-on experience with at least one major cloud provider
- Azure and Databricks preferred, but strong experience with AWS or GCP translates
- Cloud platforms: hands-on experience with at least one major cloud provider
Compensation
- Knit Health offers a competitive compensation package that includes base salary, equity, and opportunities for advancement.
- The starting salary range for the Data Engineer II is approximately $110,000 to $135,000 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
- coverage begins on the first day of employment.
- Additional benefits include a 401(k) plan and 24 days of PTO annually.
- Knit Health is an
- About Knit Health
- Knit Health is building a novel clinical foundation model to improve the way healthcare is delivered.
- We are venture backed and have partnered with multiple US-based health systems and data providers.
Company info
- What we're looking for
Equal opportunity
- equal opportunity employer and is committed to a diverse workplace.
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This listing is sourced directly from Knit Health's careers page and normalized into a canonical job model.