Senior Data Platform Engineer

Komodo Health is hiring a Senior Data Platform Engineer for its Data Foundations team. The role builds and operates large-scale data pipelines and foundational data products that power the Healthcare Map, analytics products, and downstream AI/ML use cases, while improving system reliability, performance, and scalability.

Responsibilities:

• Build, operate, and optimize large-scale production data pipelines using Python, SQL, Airflow, cloud infrastructure, and distributed processing frameworks
• Transform massive healthcare claims, EHR, and reference datasets into trusted, performant Healthcare Map data products and serving-ready data assets
• Strengthen pipeline reliability through data quality checks, validation, lineage, observability, monitoring, and alerting
• Debug complex data, system, and performance issues across computationally intensive workflows
• Partner with Data Product Quality, Product, Platform, and Engineering teams to translate healthcare data needs into scalable technical solutions
• Contribute to system design, architecture, code quality, testing, documentation, CI/CD, and rotational production support
• Enable downstream analytics, product, and AI/ML use cases through high-quality, well-modeled, reliable data

Requirements:

• Healthcare data experience across claims, clinical, RWE, provider, patient, or life sciences datasets, including coding systems such as ICD-10, CPT, NDC, or NPI
• Strong hands-on experience building, operating, and debugging production-grade data pipelines at scale
• Advanced Python and SQL skills, with experience in Airflow or similar workflow orchestration tools
• Experience with Spark or comparable distributed data processing frameworks
• Proven experience designing and operating data solutions in AWS
• Strong instincts for data quality, reliability, root-cause analysis, and production troubleshooting
• Ability to communicate technical trade-offs clearly and collaborate with engineering, product, and data partners
• Comfort using AI-assisted engineering tools for productivity, debugging, documentation, and technical exploration
• Experience delivering external-facing data products through customers, APIs, serving layers, or production access patterns
• Ability to optimize high-scale data architectures for performance, cost, versioning, and large-volume productization
• Experience applying AI or agentic workflows to engineering, data quality, delivery, or operations
• Success in high-growth or ambiguous environments that require balancing architecture, speed, and quality