Senior Data Platform Engineer

We Breathe Life Into Data

At Komodo Health, our mission is to reduce the global burden of disease. And we believe that smarter use of data is essential to this mission. That’s why we built the Healthcare Map — the industry’s largest, most complete, precise view of the U.S. healthcare system — by combining de-identified, real-world patient data with innovative algorithms and decades of clinical experience. The Healthcare Map serves as our foundation for a powerful suite of software applications, helping us answer healthcare’s most complex questions for our partners. Across the healthcare ecosystem, we’re helping our clients unlock critical insights to track detailed patient behaviors and treatment patterns, identify gaps in care, address unmet patient needs, and reduce the global burden of disease.

As we pursue these goals, it remains essential to us that we stay grounded in our values: be awesome, seek growth, deliver “wow,” and enjoy the ride. At Komodo, you will be joining a team of ambitious, supportive Dragons with diverse backgrounds but a shared passion to deliver on our mission to reduce the burden of disease — and enjoy the journey along the way.

The Opportunity at Komodo Health

Join Komodo Health’s Data Foundations team and play a critical role in shaping the core data products that fuel our Healthcare Map. You’ll transform massive, complex healthcare datasets into performant, trustworthy data assets that power customer-facing products, internal innovation, and AI/ML use cases. By building and scaling our foundational data systems, you’ll directly enable the transparency and efficiency required to drive better health outcomes across the industry.

Mission

The Senior Data Platform Engineer will design, build, operate, and improve the large-scale pipelines and foundational data platform behind Komodo’s Healthcare Map. This is a hands-on engineering role focused on processing complex healthcare data at scale, improving reliability and performance, and contributing to the technical direction of core data systems.

Looking back on your first 12 months at Komodo Health, you will have…

• Architectural Advancement: Delivered high-impact technical initiatives that improve pipeline performance, scalability, and system efficiency.
• Platform Hardening: Improved the reliability, observability, and cost-efficiency of core Data Foundations systems.
• Healthcare Data Innovation: Built deep domain expertise and contributed new approaches to challenges such as patient journey mapping and identity resolution.
• Cross-Functional Delivery: Partnered with Data Product and Engineering teams to ship scalable, production-grade data solutions.
• Architecture Partnership: Raised the bar across the team through mentorship, design reviews, and engineering best practices.

Key Responsibilities

• Build, operate, and optimize large-scale batch pipelines using Python, SQL, Spark, and Airflow on AWS
• Make storage and data layout decisions (table formats, partitioning, file sizing, compaction) that keep large datasets fast to query and cost-efficient
• Ingest data from many external sources and keep pipelines correct through frequent schema changes
• Transform healthcare claims, EHR, and reference data into trusted, serving-ready data products for customers and internal teams
• Strengthen reliability through data quality checks, validation, lineage, observability, and alerting
• Debug complex data, system, and performance issues across compute-heavy workflows
• Contribute to system design, code quality, testing, documentation, CI/CD, and rotational production support

What you bring to Komodo Health:

• 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
• A 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
• The 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

AI-Augmented Engineering Expectations:

• You will be expected to leverage AI-augmented engineering tools, such as ChatGPT, Gemini, or Claude, to improve productivity and technical decision-making. This may include using AI to generate and refine code, accelerate documentation, automate test case creation, debug complex issues, explore unfamiliar technical concepts, and assess architectural trade-offs and risks.

Additional skills and experience we’d prioritize (nice to have)…

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