Axle
Data Engineer
About this role
Axle, a bioscience and IT company, seeks a Data Engineer in Rockville, MD to design and maintain data pipelines supporting biomedical research and clinical data integration. You'll collaborate with scientists and developers to build scalable, secure, and well-governed data solutions that enable research discovery and analytics.
What you'll do
- Design, build, and optimize data pipelines and workflows for biomedical dataset ingestion, transformation, and harmonization
- Ensure data quality, validation, documentation, and accessibility for research teams and applications
- Collaborate with scientists, researchers, data scientists, and developers on data integration initiatives
- Support complex multi-source dataset management and research data lifecycle processes
- Develop ETL/ELT solutions and contribute to data governance and security practices
- Support analytics and application-facing data products aligned with organizational priorities
What they're looking for
- Python
- SQL
- ETL/ELT development
- Data modeling
- Data quality management
- Biomedical/clinical data experience
- Data governance
- Cloud or scalable data platforms
Benefits
- 100% Medical, Dental & Vision Coverage
- Paid Time Off and Paid Holidays
- 401K match up to 5%
- Educational Benefits for Career Growth
- Flexible Spending Accounts (Healthcare, Parking, Dependent Care, Transportation)
- Employee Referral Bonus
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Axle
Axle is a bioscience and IT company that builds data pipelines, identity management systems, and computational platforms supporting biomedical research, clinical data integration, and pharmaceutical innovation. The company is hiring Quality Assurance Engineers, Data Engineers, Computer Programmers, Bioinformatics Engineers, and AI/ML Scientists to develop scalable, secure infrastructure and advanced analytics capabilities for research organizations and NIH-funded initiatives.
View all jobs at AxleLikely interview questions
- Walk us through your experience designing and building ETL/ELT pipelines. What tools and languages have you used, and how do you ensure data quality throughout the process?
- Describe your approach to data modeling for complex, multi-source datasets. How do you handle data harmonization and validation across different biomedical or research data sources?