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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
• Help establish and uphold standards for data quality, testing, documentation, and observability across the stack
• Navigate the complex and often ambiguous landscape of healthcare data, bringing clarity, organization, and thoughtful structure to messy problem spaces
• Contribute to architectural decisions that will shape how we work with data at scale

We're looking for candidates who meet one of the following:

• 2-5 years of professional experience specifically in data engineering (building data pipelines, ETL/ELT workflows, data modeling, and warehouse architectures)
• 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
• A combination of internships, research, and substantial project experience that clearly demonstrates equivalent data engineering capability

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.).

• Engineering fundamentals: comfort with version control (Git), code review, testing, and the habits of writing code others can read, maintain, and trust
• SQL: strong command of joins, window functions, CTEs, and aggregate logic; a basic understanding of query performance and when to worry about it
• Python: fluency writing clean, modular code for data manipulation, transformation, and scripting; familiarity with common libraries such as pandas and at least one testing framework (pytest or similar)
• ML data processing: An understanding of basic machine learning and AI concepts as well as an understanding of the typical AI/ML data workflows.
• Spark / distributed processing: working familiarity with PySpark and an understanding of how distributed compute differs from single-machine workflows
• Cloud platforms: hands-on experience with at least one major cloud provider; Azure and Databricks preferred, but strong experience with AWS or GCP translates
• Data engineering concepts: a solid grounding in batch and streaming processing, data modeling, orchestration, data quality, governance, and database fundamentals (both relational and columnar)
• Communication: the ability to explain technical tradeoffs clearly, in writing and in conversation, to both engineers and non-engineers
• Healthcare: Prior exposure to healthcare data or the healthcare domain more broadly

• Familiarity with healthcare interoperability standards such as FHIR and HL7
• Awareness of healthcare privacy and compliance frameworks (HIPAA, BAAs, and similar)
• An eye for compute cost structures and the instincts to build with efficiency in mind

In your first few months, you'll get deep exposure to our existing data infrastructure, our healthcare data sources, and the research and product workflows your pipelines support. By the end of your first year, we'd expect you to:

• Own meaningful pieces of our data platform end-to-end, from design through production
• Lead the integration of a new data source or domain, including its modeling, quality safeguards, and downstream interfaces
• Have raised the bar somewhere - whether in testing, documentation, cost, reliability, or developer experience
• Be a trusted collaborator to our data science and ML teams, shaping how they work with data rather than just responding to requests

• You'll report to our Director of Data Engineering
• You'll work alongside the broader data science team on shared infrastructure, tooling, and data problems
• You'll partner closely with our core model AI team, i.e. the engineers and researchers who consume your data for model training, in a tight feedback loop where data quality directly shapes model performance
• You'll have real visibility into how your work lands downstream and the impact it has on foundation model training

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.