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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Platform Engineer - **Company:** Komodo Health - **Location:** Chicago, IL, United States (Remote available) - **Experience:** Expert - **Salary:** $196,000.0 - $230,000.0 - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Big Data, Software Quality, Continuous Integration, Directed Acyclic Graph (Directed Graphs), Data Validation, Data Files, Data Governance, Data Infrastructure, Data Systems, Software Debugging, Distributed Computing Environment, Python (Programming Language), Operational Databases, Reference Data, Software Tools, Standard Sql, SQL Databases, Test Case, Apache Spark, Electronic Medical Records, Agentic-AI, Data Lakes, Core Data, Kubernetes, Claude, Google Gemini, GPT, Docker - **Published:** October 29, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-data-platform-engineer-chicago-il--4ca11c57-7998-4e8e-9cad-2a486fa8108d ## About the Role Disease, Ecosystems, Clinical Medicine, Algorithms, Healthcare, Customer Support/Service, Customer/Client Research, Diversity, Data Sets, Customer Relations, * 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. ## Description 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., * 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