Senior Healthcare Data & Integration Engineer

📍 Location: Remote

🕒 Type: Full-time

Rate: 4.000 USD

🧭 About the Role

We are building an enterprise healthcare workflow platform that depends on reliable, normalized, and operationally safe healthcare data.

We are looking for a Senior Healthcare Data & Integration Engineer to own the ingestion, normalization, mapping, and data quality infrastructure that powers workflows, analytics, and future workflow intelligence capabilities.

This is not a generic data engineering role. You will work directly with real-world healthcare data across fragmented systems and inconsistent formats — including APIs, HL7v2 feeds, FHIR resources, C-CDA documents, batch files, and operational extracts.

You will solve complex problems involving inconsistent schemas, missing fields, duplicate identifiers, late-arriving events, source-system drift, and patient identity reconciliation.

This role is ideal for someone who enjoys building reliable data infrastructure, working deeply with healthcare interoperability challenges, and creating systems that transform messy operational data into trusted enterprise-grade datasets.

🎯 What You’ll Build

1. Healthcare Data Ingestion Pipelines

• Build and maintain connectors for healthcare APIs, FHIR, HL7v2, C-CDA, SFTP/file feeds, and batch ingestion systems
• Develop scalable batch and event-driven ingestion pipelines
• Ensure reliable ingestion across fragmented and inconsistent healthcare systems

2. Data Normalization & Canonical Modeling

• Map source-system data into internal canonical healthcare models
• Implement normalization for codes, timestamps, identifiers, and source mappings
• Build validation and schema enforcement logic for healthcare data consistency

3. Patient Identity & Timeline Construction

• Design patient identity linkage and crosswalk strategies across MRNs, encounter IDs, and external identifiers
• Build encounter, episode, and event timeline logic
• Handle duplicate records, late-arriving events, replay scenarios, and idempotent processing

4. Data Quality & Operational Visibility

• Implement data quality checks, schema drift detection, lineage tracking, and reproducible pipeline execution
• Build operational reporting for ingestion failures, incomplete records, and source-system inconsistencies
• Ensure pipeline reliability through monitoring, alerting, and failure recovery systems

5. Platform & Workflow Collaboration

• Partner closely with backend/platform engineers to ensure ingested data can safely trigger governed workflows
• Collaborate with customer and integration stakeholders to understand source-system behavior and operational constraints
• Prepare clean, validated datasets for analytics, workflow intelligence, and future ML use cases

📊 What Success Looks Like

• Reliable and reproducible healthcare ingestion pipelines
• High-quality normalized healthcare datasets
• Accurate patient identity resolution and timeline construction
• Strong operational visibility into pipeline health and failures
• Stable integrations across fragmented healthcare systems
• Trusted data infrastructure supporting enterprise workflows and analytics

🧬 Required Qualifications

Must-Have

• 6–10+ years of experience in data engineering, integration engineering, or healthcare interoperability
• Strong Python experience
• Experience building both batch and event-driven data pipelines
• Experience working with messy, inconsistent, real-world source data
• Strong understanding of data validation, lineage, replayability, and reproducibility
• Experience with PostgreSQL
• Experience working with object storage systems (S3-compatible storage preferred)
• Experience building reliable pipelines with monitoring, testing, alerting, and recovery mechanisms
• Strong debugging skills across APIs, files, timestamps, identifiers, and schema inconsistencies
• Ability to collaborate closely with backend engineers on data contracts and workflow-triggering requirements

Strongly Preferred

• HL7v2 experience
• FHIR experience
• C-CDA experience
• Experience with OMOP or healthcare canonical data models
• Experience working within hospital IT environments
• Experience with healthcare integration engines or interoperability platforms
• Experience with patient identity matching, MRN crosswalks, encounter modeling, or healthcare timeline systems
• Experience with data validation tools such as Great Expectations, Soda, or dbt tests
• Experience handling PHI-sensitive data and healthcare privacy requirements

🔎 Ideal Profile

• Deeply technical and systems-oriented data engineer
• Comfortable working with imperfect and operationally messy healthcare data
• Strong focus on reliability, traceability, and operational correctness
• Able to balance speed with long-term maintainability and data integrity
• Strong debugging and analytical problem-solving abilities
• Collaborative engineer who works effectively across backend, platform, and customer integration teams

⚡️ Why Join

• Opportunity to solve complex healthcare interoperability and data engineering challenges
• Work on foundational infrastructure powering enterprise healthcare workflows
• High ownership and technical influence over critical platform systems
• Build systems that directly impact operational reliability and workflow intelligence
• Collaborative engineering environment focused on solving real-world healthcare problems