.NET Full stack Developer

.NET Full stack Developer – Little Canada, MN 55117 (Hybrid/Onsite) 12+ Months Contract – Pay: $75-$80/hr on W2

Note
:
Data Engineering Experience required.

We are seeking a Full Stack Developer with strong experience in backend development using C#/.NET and frontend development using Angular, along with solid data engineering skills. This role will support the development and enhancement of a data platform used for ingesting, managing, and analyzing large-scale imaging and electrophysiology procedural datasets. The ideal candidate is hands‑on across the full stack and comfortable working with data pipelines, APIs, and scalable enterprise systems.

• Design and develop full stack web applications for data ingestion, management, visualization, and labeling
• Build and maintain backend services and REST APIs to support data workflows
• Design and implement data pipelines for ingesting, transforming, and storing large datasets
• Integrate data from multiple external systems and sources
• Support longitudinal data tracking and versioning across datasets
• Collaborate closely with data scientists, R&D engineers, and domain experts
• Ensure performance, reliability, and maintainability of the platform
• Follow best practices for data security, privacy, and compliance

• Full Stack Development
  • Strong experience in full stack development (frontend and backend)
  • Strong experience with C# and .NET (.NET Core / .NET Framework)
  • Frontend:
    Strong experience with Angular (Type Script, HTML, CSS)
  • Experience with RESTful APIs and microservices architecture
• Data Engineering
  • Experience designing and building data pipelines (ETL/ELT)
  • Strong data modeling skills
  • Experience with Postgre
    
    SQL and No
    
    SQL databases
  • Familiarity with cloud based data storage and compute (Azure preferred)

• Experience working with large imaging and time series datasets
• Exposure to healthcare, clinical, or regulated data environments
• Familiarity with data anonymization or pseudonymization techniques
• Experience supporting ML/AI data preparation workflows
• Dev Ops experience (CI/CD pipelines, containerization)

• Scalable and reliable data ingestion and management capabilities
• Well documented, production quality code
• Improved support for longitudinal and multi‑source data workflows
• Close collaboration with internal teams to meet project timelines