Design, build, and scale backend systems that power scientific and geospatial products
Build and maintain public APIs in Python, including versioning and documentation
Develop and maintain a robust data access layer (DAL) and well-structured data models
Process large, complex datasets and geospatial information with Python data libraries
Collaborate closely with science and product teams to turn requirements into reliable production services
Occasionally integrate existing ML models into services or pipelines (integration-oriented)
Ensure performance, correctness, cost efficiency, and scalability of geospatial data pipelines and services
Work in Agile sprints, participate in daily standups, and deliver backlog items
Contribute beyond code: documentation, tests, deployments, release notes, and demos
Participate in initiatives focused on scaling pipeline capacity and improving non-functional characteristics
Requirements
Bachelor's or Master's degree in Computer Science, Engineering, or a related field
3+ years of experience building production-grade Python services with a focus on public APIs, data processing, performance, and scalability
Strong experience with Python web frameworks and API tooling, such as FastAPI or Flask
Schema validation experience (Pydantic)
Solid understanding of data modeling and a clean, maintainable DAL (e.g., SQLAlchemy), including migrations and versioned schemas
Proficiency with Python data libraries such as NumPy and pandas
Strong understanding of engineering principles and practices: separation of concerns, encapsulation, code reviews, CI, observability, and layered testing strategies
Autonomous but collaborative working style; ability to own meaningful components
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