Urgent Requirement :: Data engineer :: Remote
Navneet Singh
Hi,
Hope you are doing good, I have the below open position with our client, please find and let me know your interest along with an updated copy of resume and best contact number to discuss further if comfortable & interested for this role.
Data engineer
Location: Remote
Exp: 8+years
Contract Role
Description:
For first data engineer hire, the requirements are extremely specific. I need someone who has worked in at least one healthcare domain — payer, provider, medical device, or population health — and has hands-on experience working with regulated health data in production environments. Just as importantly, they must have been part of a healthtech startup and understand what it takes to go from 0 to 1 with limited resources, urgency, and high accountability.
Healthcare is not something one can learn quickly. The ecosystem, compliance landscape, and data structures are deeply complex. It’s critical to understand what the data means and how it impacts outcomes so someone who already knows how to navigate that environment on day one will be an ideal fit.
The ideal candidate must already demonstrate the following:
• Experience with payer, provider, or population health datasets including risk adjustment, utilization, care gaps, and attribution
• Deep familiarity with healthcare data standards: FHIR, HL7, CCDA, EDI 837/835
• Eligibility and enrollment transaction expertise: EDI 834, 270 / 271
• EMR/EHR integrations and mapping of encounter-level and longitudinal patient records
• Extensive expertise or exposure in clinical and billing code systems, including:
o ICD-10 (diagnoses)
o CPT / HCPCS (procedures and services)
o SNOMED CT (clinical concepts)
o LOINC (lab/diagnostic observations)
o NDC (medications)
o DRGs & APCs (inpatient/outpatient payment groupers)
o RxNorm (standardized drug terminology)
o HCC models for risk adjustment
• Hands-on experience with Epic data and/or open-source EMR systems such as OpenMRS or OpenEMR
• Building HIPAA-compliant data architectures with encryption, RBAC, audit trails, and full PHI governance
• Interoperability across claims, clinical, pharmacy, and patient-generated data ecosystems
• Data modeling and pipelines designed for AI/ML workloads and regulatory reporting
• Ability to move quickly in a startup environment without compromising compliance or quality
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