Data Engineer - Health Metrics & FHIR Pipelines

Socket.dev; Durham (NC); On-site; USD 110,000 - 160,000; Full time

Benefits offered by this job

Competitive salary and benefits

Growth opportunities

Collaborative culture

Impact on healthcare

Professional development

Job summary

Socket.dev is seeking a mid to senior Data Engineer to design and optimize healthcare data infrastructure. You will build scalable ETL/ELT pipelines on AWS, work with SQL, Python, and dbt, and contribute to data models aligned with FHIR and HEDIS standards.

You will collaborate with stakeholders to integrate population health analytics and ensure HIPAA-compliant data handling while leveraging tools like Redshift Serverless and Temporal for reliability and scalability.

Qualifications

• US-based candidates only.
• Proven experience in data engineering roles with SQL, Python, and AWS data infrastructure.
• Experience with dbt and Spark/PySpark for transformation and modeling.
• Familiarity with healthcare data systems, including HEDIS, population health, and FHIR data models.

Responsibilities

• Design, build, and maintain scalable data pipelines for ETL/ELT on AWS.
• Develop, test, and deploy robust SQL/Python data solutions.
• Implement and manage data warehousing using Redshift Serverless and AWS services.
• Utilize dbt for data modeling, transformation, and documentation.
• Use Temporal for pipeline orchestration and automation.

Skills

SQL

Python

AWS

HEDIS metrics

FHIR data models

Data warehousing

Workflow orchestration

dbt

Spark/PySpark

Tools

dbt

Spark/PySpark

Temporal

Airflow

Dagster

Terraform

Redshift Serverless

Job description

Socket.dev is seeking a mid to senior Data Engineer to design and optimize healthcare data infrastructure. You will build scalable ETL/ELT pipelines on AWS, work with SQL, Python, and dbt, and contribute to data models aligned with FHIR and HEDIS standards.

You will collaborate with stakeholders to integrate population health analytics and ensure HIPAA-compliant data handling while leveraging tools like Redshift Serverless and Temporal for reliability and scalability.