Senior Data Platform Engineer (Healthcare Data)

Location: Open to remote work in the United States or hybrid work in San Francisco or New York City.

Annual base pay: $196,000–$230,000 for the San Francisco Bay Area and New York City; $170,000–$200,000 for all other US locations. Performance-based bonuses and equity may also be available.

Company

hirify.global develops healthcare data products and software based on de-identified real-world patient data, algorithms, and clinical expertise to support healthcare insights and reduce the burden of disease.

What you will do

• Build, operate, and optimize large-scale batch pipelines using Python, SQL, Spark, Airflow, and AWS.
• Make storage and data-layout decisions covering table formats, partitioning, file sizing, and compaction.
• Ingest external data sources and maintain pipeline correctness through frequent schema changes.
• Transform healthcare claims, EHR, and reference data into trusted, serving-ready data products.
• Improve reliability through data quality checks, validation, lineage, observability, alerting, testing, documentation, and CI/CD.
• Debug complex data, system, and performance issues and provide rotational production support while collaborating with engineering, product, and data teams.

Requirements

• Strong hands-on experience building, owning, and debugging production data pipelines at multi-terabyte scale.
• Advanced Python for performant, memory-efficient large-scale data processing and strong SQL skills.
• Deep production experience operating and scaling Airflow, beyond writing DAGs.
• Understanding of physical data storage, including columnar and row formats and open table formats such as Iceberg, Delta Lake, or Hudi, including updates, compaction, small files, and partitioning.
• Experience with Spark or a comparable distributed processing framework.
• Proven experience designing and operating data systems in AWS.
• Strong data-correctness, root-cause-analysis, and production-troubleshooting skills.
• Ability to communicate technical trade-offs clearly with engineering, product, and data partners.
• Comfort using AI-assisted engineering tools such as ChatGPT, Gemini, or Claude for coding, debugging, documentation, testing, technical exploration, and architectural analysis.

Nice to have

• Experience with containers and orchestration, including Docker and Kubernetes.
• Strong core software engineering fundamentals and experience with more than one programming 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.

Culture & Benefits

• Hybrid work model with hubs in San Francisco, New York City, and Chicago; role-specific remote and hub-based arrangements.
• Health, dental, and vision insurance.
• Flexible time off, holidays, disability insurance, life insurance, and applicable leaves of absence.
• 401(k) with company match.
• Performance-based bonuses and potential equity awards.
• AI is expected to be integrated into daily engineering work, including workflow automation and insight generation.