SafeRide Health

Data Engineer - Healthcare Claims

SafeRide Health United States · $80K–$150K/yr

Hospitals and Health Care · 501-1,000 employees

5 d ago
Remote data-engineer Mid (2-5 yrs) Full-time United States
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About the role

You will design and implement a scalable claims data model and architecture while collaborating with AI teams to build end-to-end solutions. Additionally, you will be responsible for extracting, cleansing, and modeling claims data to ensure high accuracy and quality for downstream operations.

What they look for

SQL Python Data modeling Data warehousing Star-schema modeling AWS Redshift Athena Airflow Healthcare claims Billing Coding Data engineering Automation Solution architecture

Requirements

Candidates must have a bachelor's degree in a technical field and at least 3 years of professional experience with SQL and data modeling. Proficiency in Python, AWS data services, and direct experience with healthcare claims or billing is required.

Benefits

Competitive salary Annual bonus opportunities Remote work Flexible hours Health insurance Dental insurance Vision insurance 401(k) with company match Generous PTO Paid company holidays Paid parental leave Career growth opportunities

Full description

About the Role:

SafeRide Health is seeking a Data Engineer or Senior Data Engineer with Healthcare Claims experience to build the data foundation for our claims capabilities.

Currently, we excel at coordinating and completing transportation - this role will strengthen the behind-the-scenes operations that transform healthcare and ride data into accurate, scalable claims solutions.

This role combines immediate hands-on data engineering with longer-term solution design. You’ll work closely with the Data and AI teams to build a claims architecture from the ground up as SafeRide Health continues to scale.

You will:

  • Extract, cleanse, organize, and model claims-related data for downstream use
  • Use SQL as a primary engineering tool, with Python for automation, validation, and solution development
  • Design and implement the claims data model and supporting pipelines
  • Partner with the AI team to develop an end-to-end claims solution using AI-assisted engineering where appropriate
  • Evaluate upstream and downstream data impacts and improve claims accuracy, quality, and scalability

Required Qualifications:

  • Bachelor’s degree in Computer Science, Engineering, Mathematics, or related technical field
  • 3+ years professional experience with SQL and data modeling
  • Proficiency with Python for data engineering, automation, and validation
  • Strong data warehousing and data modeling experience, including star-schema modeling and report design

• AWS Data Services (Redshift, Athena, S3, DMS, Glue, SageMaker, etc.)

  • Experience with workflow orchestration tools like Airflow
  • Experience building data solutions in a high-scale or high-expectation engineering environment
  • Demonstrated ownership and ability to design and implement solutions in a rapidly transforming environment
  • Direct healthcare claims, billing, coding, or related data experience

What Success Looks Like:

  • Develop and own the Claims Data Model
  • Design and implement the Claims Solution Architecture
  • Achieve and maintain Claims Accuracy

Benefits:

  • Competitive salary and annual bonus opportunities
  • Remote with flexible hours
  • Comprehensive health, dental, and vision insurance
  • 401(k) with company match
  • Generous PTO, 10 paid company holidays, and paid parental leave
  • Career growth opportunities in a mission-driven organization

About Us:

SafeRide Health is a technology and services company committed to eliminating transportation barriers to healthcare. By combining our proprietary tech platform with a nationwide network of vetted transportation providers, we empower payers and health systems to deliver smarter, more reliable non-emergency medical transportation.

Our platform supports the nation’s largest Medicare Advantage, Medicaid, and provider programs - improving health outcomes and patient experiences at scale.

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