EHR Clinical Data Integration Engineer

Launch Ventures Private Limited

Job Location:

Hyderabad - India

Monthly Salary: Not Disclosed

Experience Required: 4-5years

Posted on: Yesterday

Vacancies: 1 Vacancy

Job Summary

Role Overview

We are looking for an EHR Clinical Data Integration Engineer who can bridge the gap between external stakeholder management and internal production engineering. You will own clinical data ingestion end-to-end serving as the primary technical liaison to health systems to establish

feeds while simultaneously building the robust production-grade data pipelines that power clinical platforms.

This role sits at the intersection of integration leadership data engineering and production readiness. You will drive the timeline for getting data in and own the architecture that keeps that data reliable.

What Youʼll Do

Integration Leadership & Sourcing (Outward Facing)

Lead Technical Engagement: Act as the primary technical liaison for external health system stakeholders. You will lead discussions to define data specs reconcile gaps and manage the integration lifecycle from kickoff to production.
Establish Feeds: configure and secure clinical data feeds from Epic Athena health and partner systems using patterns like FHIR APIs HL7v2 feeds SFTP/file exchange or partner specific mechanisms.
Scope & Validate: Validate client-ready data requests and work with care teams to ensure requirements are comprehensive before technical implementation begins.

Data Engineering & Platform Integration (Internal Facing)

Build Scalable Pipelines: Design and maintain production-grade ETL/ELT services using Python and . You will implement data engineering best practices (e.g. Medallion architecture concepts) to ensure data is organized accessible and performant.
Orchestration & Automation: Utilize orchestration tools (e.g. Airflow Dagster) or data platforms to automate workflows ensuring data freshness and reliability.
Platform Integration: Integrate pipelines with the clinical platform ensuring secure handling of PHI consistent data contracts and seamless downstream consumption.

Data Quality & Reliability

Validation Frameworks: Implement robust validation frameworks and QC checks (completeness conformance plausibility reconciliation/back testing) to catch issues before they reach production.
Root Cause Analysis: Create repeatable workflows for debugging data issues across source systems transformations and downstream consumers.
Observability: Define and monitor SLAs/SLOs for pipeline health using AWS CloudWatch (logs metrics alarms) ensuring high availability and rapid incident response.

Requirements

Must Have

3 to 4 years of experience in data engineering backend engineering or integration engineering
Healthcare Data Expertise: Direct experience establishing clinical data feeds from Epic Athena health Cerner or eClinicalWorks with a deep understanding of data standards (FHIR HL7v2 CCD/C-CDA).
Strong Data Engineering Fundamentals: Experience building and maintaining scalable data pipelines using modern orchestration tools (Airflow Dagster) or data platforms (Data bricks dbt). Candidates should understand concepts like medallion architecture.
Strong Programming Skills:

Python: For ETL validation tooling and backend services.
For building integration services and APIs.
SQL: For complex querying profiling and reconciliation.
Data Quality Mindset: Proven track record of building automated QC checks and validation logic that prevents bad data from entering production.

Stakeholder Management: Experience acting as a technical lead or liaison managing timelines and expectations with external partners or clients.
AWS Production Operations: Comfortable operating workloads in AWS (IAM networking) and using CloudWatch for observability (logs metrics dashboards).

Nice to Have

Advanced Observability: Experience with Grafana Datadog Prometheus or OpenTelemetry.
Distributed Processing: Experience with PySpark or high-volume ingestion patterns.
Infrastructure as Code: Exposure to Terraform CloudFormation or similar tools.
Containerization: Experience with Docker Kubernetes (EKS) and CI/CD (GitHub Actions).
AI/LLM-Assisted Automation: Interest or experience in using agentic workflows (e.g. LangChain AutoGPT or custom LLM scripts) to automate data mapping schema reconciliation or documentation tasks.

What Success Looks Like (First 60 to 90 Days)

Map the Architecture: Establish a clear picture of. clinical data flows data contracts and integration architecture.
Ship Reliability: Implement at least one measurable improvement to data quality (e.g. better QC checks automated back testing or reduced incident volume).
Improve Operations: Reduce time-to-detect/time-to-resolve for data issues via better CloudWatch dashboards and Run books.
Streamline Onboarding: Improve the onboarding process for new health system integrations with clearer technical specifications and reusable integration patterns.

Required Skills:

Must Have 3 to 4 years of experience in data engineering backend engineering or integration engineering Healthcare Data Expertise: Direct experience establishing clinical data feeds from Epic Athena health Cerner or eClinicalWorks with a deep understanding of data standards (FHIR HL7v2 CCD/C-CDA). Strong Data Engineering Fundamentals: Experience building and maintaining scalable data pipelines using modern orchestration tools (Airflow Dagster) or data platforms (Data bricks dbt). Candidates should understand concepts like medallion architecture. Strong Programming Skills: Python: For ETL validation tooling and backend services. For building integration services and APIs. SQL: For complex querying profiling and reconciliation. Data Quality Mindset: Proven track record of building automated QC checks and validation logic that prevents bad data from entering production. Stakeholder Management: Experience acting as a technical lead or liaison managing timelines and expectations with external partners or clients. AWS Production Operations: Comfortable operating workloads in AWS (IAM networking) and using CloudWatch for observability (logs metrics dashboards). Nice to Have Advanced Observability: Experience with Grafana Datadog Prometheus or OpenTelemetry. Distributed Processing: Experience with PySpark or high-volume ingestion patterns. Infrastructure as Code: Exposure to Terraform CloudFormation or similar tools. Containerization: Experience with Docker Kubernetes (EKS) and CI/CD (GitHub Actions). AI/LLM-Assisted Automation: Interest or experience in using agentic workflows (e.g. LangChain AutoGPT or custom LLM scripts) to automate data mapping schema reconciliation or documentation tasks. What Success Looks Like (First 60 to 90 Days) Map the Architecture: Establish a clear picture of. clinical data flows data contracts and integration architecture. Ship Reliability: Implement at least one measurable improvement to data quality (e.g. better QC checks automated back testing or reduced incident volume). Improve Operations: Reduce time-to-detect/time-to-resolve for data issues via better CloudWatch dashboards and Run books. Streamline Onboarding: Improve the onboarding process for new health system integrations with clearer technical specifications and reusable integration patterns.

Role OverviewWe are looking for an EHR Clinical Data Integration Engineer who can bridge the gap between external stakeholder management and internal production engineering. You will own clinical data ingestion end-to-end serving as the primary technical liaison to health systems to establishfeeds w...