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Salary: USD 196,000 - 230,000 / annual
Komodo Health is building the Healthcare Map, the industry's largest and most complete view of the U.S. healthcare system by combining de-identified real-world patient data with innovative algorithms and clinical expertise. The company's mission is to reduce the global burden of disease through smarter use of data.
You will join Komodo Health's Data Foundations team as a Senior Data Platform Engineer, playing a critical role in shaping the core data products that fuel the Healthcare Map. This is a hands-on engineering role focused on designing, building, operating, and improving large-scale pipelines and foundational data systems that process complex healthcare datasets at scale.
In your first 12 months, you will deliver high-impact technical initiatives that improve pipeline performance, scalability, and system efficiency; harden core Data Foundations systems for reliability, observability, and cost-efficiency; build deep domain expertise in healthcare data challenges such as patient journey mapping and identity resolution; partner with Data Product and Engineering teams to ship scalable, production-grade data solutions; and raise the bar across the team through mentorship, design reviews, and engineering best practices.
Key responsibilities include: building, operating, and optimizing large-scale batch pipelines using Python, SQL, Spark, and Airflow on AWS; making storage and data layout decisions (table formats, partitioning, file sizing, compaction) to keep large datasets fast and cost-efficient; ingesting data from many external sources and maintaining pipeline correctness through frequent schema changes; transforming healthcare claims, EHR, and reference data into trusted, serving-ready data products; strengthening reliability through data quality checks, validation, lineage, observability, and alerting; debugging complex data, system, and performance issues across compute-heavy workflows; and contributing to system design, code quality, testing, documentation, CI/CD, and rotational production support.
Komodo emphasizes AI-augmented engineering and expects you to leverage tools like ChatGPT, Gemini, or Claude to improve productivity and technical decision-making, including code generation, documentation, test automation, debugging, and architectural assessment.
The company operates a hybrid model with hubs in San Francisco, New York City, and Chicago. This role is open to US remote or SF/NYC hybrid arrangements.
REQUIREMENTS:
- Strong hands-on experience building, owning, and debugging production data pipelines at multi-terabyte scale
- Advanced Python, including performant, memory-efficient code for large-scale data processing, plus strong SQL
- Deep production experience with Airflow, including operating and scaling it, not just writing DAGs
- Solid understanding of how data is physically stored: columnar vs. row formats, and open table formats like Iceberg, Delta Lake, or Hudi (updates, compaction, small files, partitioning)
- Experience with Spark or a comparable distributed processing framework
- Proven experience designing and operating data systems in AWS
- Strong instincts for data correctness, root-cause analysis, and production troubleshooting
- Ability to communicate technical trade-offs clearly with engineering, product, and data partners
- Comfort using AI-assisted engineering tools for coding, debugging, documentation, and technical exploration
NICE-TO-HAVE:
- Containers and orchestration (Docker, Kubernetes)
- Strong core software engineering fundamentals and experience in more than one language
- Data governance and lineage experience
- Experience delivering external-facing data products through APIs, serving layers, or customer access patterns
- Experience applying AI or agentic workflows to engineering, data quality, delivery, or operations
- Success in high-growth or ambiguous environments
No healthcare background required; domain expertise will be built on the job.
