Senior Data Platform Engineer

Komodo Health is a healthcare data and technology company focused on reducing the global burden of disease through its Healthcare Map and software applications. The Senior Data Platform Engineer will design, build, operate, and improve large-scale data pipelines and foundational data products that support healthcare analytics, customer-facing applications, and AI/ML use cases. The role also focuses on reliability, performance, data quality, architecture, and cross-functional delivery.

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
• Open to US remote, OR SF/NYC hybrid
• 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