(ID: 2026-1524)

Axle is a bioscience and information technology company that offers advancements in translational research, biomedical informatics, and data science applications to research centers and healthcare organizations nationally and abroad. With experts in biomedical science, software engineering, and program management, we focus on developing and applying research tools and techniques to empower decision-making and accelerate research discoveries. We work with some of the top research organizations and facilities in the country including multiple institutes at the National Institutes of Health (NIH).

Benefits We Offer:

100% Medical, Dental & Vision Coverage for Employees
Paid Time Off and Paid Holidays
401K match up to 5%
Educational Benefits for Career Growth
Employee Referral Bonus
Flexible Spending Accounts:
- Healthcare (FSA)
- Parking Reimbursement Account (PRK)
- Dependent Care Assistant Program (DCAP)
- Transportation Reimbursement Account (TRN)

About the Mission
Join the team at the forefront of revolutionizing medical research in the United States. We are building and maintaining the foundational infrastructure of the National Clinical Cohort Collaborative (N3C)—the nation’s largest and most significant public repository of harmonized electronic health record (EHR) data.

What began as a critical response to the COVID-19 pandemic has evolved into a multi-disease, terabyte-scale data resource that enables researchers across the country to accelerate discovery and improve public health outcomes. The platform integrates EHRs, claims, registries, and other data sources in a secure, regulated environment to support thousands of scientists.

This role is an opportunity to contribute to the core data platform that makes this research possible.

The Role
We are seeking a mid-level Data Platform Engineer to help build and operate the core data infrastructure that powers large-scale, regulated healthcare and research datasets. This role is ideal for an engineer who has moved beyond “entry level,” understands how production systems behave, and wants to grow into owning complex pipelines, orchestration logic, and platform reliability.

You’ll work alongside senior engineers and informatics experts to design, implement, and maintain ingestion, transformation, orchestration, and data quality systems that are reliable, observable, and secure.

What You’ll Do

Build Production-Grade Data Systems

Write clean, modular, well-tested Python code for data pipelines and platform services.
Use decorators, context managers, and unit tests to ensure correctness and maintainability.
Contribute to shared libraries and reusable components across the platform.

Design and Maintain Data Models

Implement relational data models aligned with medallion architectures (bronze/silver/gold).
Support schema evolution and backward-compatible changes.
Work with modern table formats such as Apache Iceberg.

Data Orchestration & Ingestion

Build and maintain data workflows using Dagster (preferred) or Airflow.
Manage sensors, schedules, and complex job dependencies.
Implement ingestion pipelines using Airbyte or similar ELT tools.

Transformation & Data Quality

Implement idempotent transformation logic using SQLMesh/Tobiko (preferred) or dbt.
Add data quality checks and validation gates using frameworks like Great Expectations.
Partner with upstream and downstream users to diagnose and resolve data issues.

Containerization & CI/CD

Build, debug, and optimize Docker images for local and production environments.
Contribute to CI/CD pipelines supporting automated testing and deployment.
Follow modern Git workflows including branching strategies, pull requests, and code reviews.

Infrastructure, Cloud & Security

Read and modify infrastructure-as-code using Terraform.
Work with AWS primitives (S3, Lambda, Glue, Fargate), with a focus on portability and migration toward open-source, cloud-agnostic alternatives.
Apply least-privilege and identity-based access concepts (OIDC/IAM).
Operate comfortably within regulated environments (HIPAA, FedRAMP).

Documentation & Collaboration

Document data flows, system architecture, and operational procedures clearly.
Collaborate closely with senior engineers, informaticists, and project stakeholders.
Participate in design reviews and contribute ideas for improving platform reliability and scalability.

What You’ll Bring
Required

2–4 years of experience in Data Engineering or Backend Software Engineering.
Strong proficiency in Python and SQL.
Solid understanding of relational theory and data modeling.
Experience working with orchestration tools (Dagster, Airflow, or similar).
Familiarity with containerization and Docker-based workflows.
Experience working with version control, CI/CD, and collaborative development practices.
Ability to write clear technical documentation.

Nice to Have

Experience with Iceberg, Airbyte, Great Expectations, SQLMesh, or dbt.
Prior work on regulated data platforms (healthcare, government, finance).
Interest in data platform architecture and long-term system evolution.