Senior Data Platform Engineer
San Francisco, CA
- Pay
- Pay not listedSimilar roles state $127K–$190K
- Work setting
- Not stated
- Employment
- Not stated
What you’ll work on
Full postingBuild, operate, and optimize large-scale batch pipelines using Python, SQL, Spark, and Airflow on AWS
Make storage and layout decisions (table formats, partitioning, compaction) for fast, cheap queries
Ingest data from many external sources and keep pipelines correct through schema changes
Turn healthcare claims, EHR, and reference data into trusted data products
Add data quality checks, lineage, observability, and alerting to strengthen reliability
From the employer’s posting
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
Tools in this posting
- Python
- SQL
- AWS
- Delta Lake
- Docker
- Kubernetes
- Spark
- Airflow
Source — Tool mentions in context
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
- 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
- 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
- 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
Additional skills and experience we’d prioritize (nice to have)… - Containers and orchestration (Docker, Kubernetes) - Strong core software engineering fundamentals and experience in more than one language
- 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
- 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)
Posting record
Report an errorActively hiringFirst seen by us 6 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Oct 24, 2026 · 6 days ago
- Recorded sightings
- 8
- Last seen by us
- Oct 30, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
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Job description
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)…
- 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
No healthcare background required. You’ll build that domain expertise here.
**Open to US remote, OR SF/NYC hybrid
#LIRemote
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.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
San Francisco, CA
No healthcare background required. You’ll build that domain expertise here. **Open to US remote, OR SF/NYC hybrid #LIRemote
More source context
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
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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