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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 Knowledge & Information team and play a critical role in shaping the core data products that fuel our Healthcare Map. This role is essential for transforming massive, complex healthcare datasets into performant, trustworthy, and usable data assets that directly power both customer-facing applications and internal product innovation. By building and scaling our foundational data systems, you will directly enable the transparency and efficiency required to drive better health outcomes across the industry.
Mission
The Senior Data Engineer will design, build, operate, and improve large-scale data pipelines and foundational data products that power Komodo’s Healthcare Map, analytics products, and downstream AI/ML-enabled use cases. 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: Deliver high-impact technical initiatives that improve pipeline performance, scalability, and system efficiency.
- Platform Hardening: Improve the reliability, observability, and cost-efficiency of core Data Foundations systems.
- Cross-Functional Delivery: Partner with Data Product and Engineering teams to ship scalable, production-grade data solutions.
You will accomplish these outcomes through the following responsibilities…
- Build, operate, and optimize large-scale production data pipelines using Python, SQL, Airflow, cloud infrastructure, and distributed processing frameworks — including robust data quality checks, validation, lineage, observability, monitoring, and alerting.
- Design and scale agentic data acquisition and extraction systems for complex, unstructured public data sources; develop LLM-powered Human-in-the-Loop (HITL) pipelines for data extraction and curation.
- Transform healthcare claims, EHR, non-claims-based, and reference datasets into trusted, performant Healthcare Map data products and serving-ready data assets.
- Contribute to system design, architecture, code quality, testing, documentation, CI/CD, and rotational production support — including debugging 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 that enable downstream analytics, product, and AI/ML use cases.
What you bring to Komodo Health:
- Strong hands-on experience building, operating, and debugging production-grade data pipelines at scale in AWS, with sharp instincts for data quality, reliability, root-cause analysis, and production troubleshooting.
- Advanced Python and SQL skills; experience with Airflow or similar orchestration tools and Spark or comparable distributed processing frameworks.
- Ability to communicate technical trade-offs clearly and collaborate across engineering, product, and data teams.
- Comfort using AI-assisted engineering tools for productivity, 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)…
- Healthcare data experience is a plus, but not required.
- 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.
- Proven success in high-growth or ambiguous environments that require balancing architecture, speed, and quality.
#LIRemote
Open to NYC/SF Hybrid or Remote
The pay range for each job posting reflects a minimum and maximum range of annual base pay that we reasonably expect to pay for this position within the US. We carefully consider multiple business-related factors when determining compensation, including job-related skills, work experience, geographic work location, relevant training and certifications, business needs and market demands.
The starting annual base pay for this role is listed below. This position may be eligible for performance-based bonuses as determined in the Company’s sole discretion and in accordance with a written agreement or plan. This role may also be eligible for equity awards. In addition, this role is eligible for benefits including, but not limited to, comprehensive health, dental, and vision insurance; flexible time off and holidays; 401(k) with company match; disability insurance and life insurance; and leaves of absence in accordance with applicable state and local laws and regulations and company policy.
Komodo's AI Standard
At Komodo, we're not just witnessing the AI revolution – we're leading it. This is a pivotal moment in time, where being first to market with AI transforms industries and sets the bar. We've already established industry leadership in leveraging AI to revolutionize healthcare, and we expect every team member to contribute. AI here isn't optional; it's foundational. We expect you to integrate AI into your daily work – from summarizing documents to automating workflows and uncovering insights. This isn't just about efficiency; it's about making every moment more meaningful, building on trust in AI, and driving our collective success.
Join us in shaping the future of healthcare intelligence.
Where You’ll Work
Komodo Health has a hybrid work model with hubs in San Francisco, New York City, and Chicago. Roles vary — some can be performed from anywhere in the country, others are scoped to a specific region, and some are based near one of our hubs. For hub-based Dragons, we're building intentional in-office rhythms alongside the flexibility that's core to how we work. Whatever your setup, expectations will always be clear before you join.
Equal Opportunity Statement
Komodo Health provides equal employment opportunities to all applicants and employees. We prohibit discrimination and harassment of any type with regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.
By submitting your application, you acknowledge that you have read and understand Komodo Health’s Privacy Notice for Employees and Contractors.
This notice explains how we collect, use, and retain applicant data.
Komodo Health New York, New York, USA Office
90 5th Avenue, 5th Floor, New York, United States, 10011
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