Senior Healthcare Data Infrastructure Specialist

2 weeks ago

Meerut, Uttar Pradesh, India beBeeData Full time ₹ 1,50,00,000 - ₹ 2,00,00,000

About the Role

We are seeking a Senior Data Engineer to spearhead the development of scalable, event-driven data pipelines that support real-time clinical workflows.

This is a critical role where you will create robust systems to ingest, track, and update clinical events in real-time.

You will work closely with software engineers, ML specialists, and our clinical partners to design, build, and operate high-quality data infrastructure.

Key Responsibilities:

  • Design, build, and operate scalable, event-driven data pipelines using standards such as FHIR and HL7.
  • Integrate with EHR/EMR systems (e.g., Epic) and develop reliable systems to ensure downstream ML systems receive high-quality, structured data for inference and decision support.
  • Establish observability, monitoring, and error handling for robust pipeline performance.
  • Collaborate with ML engineers to align data flows with model needs (e.g., document ingestion, embedding storage, inference triggers).
  • Continuously improve pipeline design to maximize scalability, resilience, and automation.

Requirements:

  • 5+ years of experience building and maintaining production-grade data pipelines.
  • Strong background in workflow orchestration and event-driven systems.
  • Proficiency with relational databases and SQL (e.g., Postgres) as well as handling unstructured data.
  • Experience operating in cloud environments and working with distributed, highly available systems.
  • Track record of delivering data infrastructure that supports ML or analytics applications.
  • Excellent communication and collaboration skills; able to drive projects independently while working as part of a cross-functional team.

Bonus Requirements:

  • Familiarity with FHIR/HL7 data modeling in production settings.
  • Experience supporting machine learning inference pipelines in real-world applications.
  • Expertise with image data.
  • Exposure to vector databases, embedding stores, or document retrieval systems.
  • Background in healthcare IT, clinical workflows, or health data analytics.

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