Senior Data Platform Engineer

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.

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

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