Python Backend Developer
Confirmed on the employer's own hiring board on Oct 15, 2026. First seen by Alion on Oct 15, 2026.
We are seeking an experienced Python Backend Developer to design, build, and optimize scalable data pipelines, robust APIs, and performant backend services. In this role, you will work closely with cross-functional teams to process large-scale environmental and financial datasets, deploy analytical models, and build high-throughput backend systems that power GIST Impact’s analytics platforms.
Responsibilities:
Architect, develop, and maintain high-performance RESTful APIs and microservices using FastAPI and Pydantic.
Design, build, and optimize scalable data pipelines and ETL workflows for large-scale financial and environmental datasets.
Manage and optimize relational databases including PostgreSQL and MySQL, focusing on schema design, query optimization, and indexing strategies.
Develop interactive data dashboards and visualization components using Plotly Dash.
Establish comprehensive automated test suites using Pytest to ensure reliability, performance, and code quality.
Lead architectural enhancements for data management systems and backend microservices.
Mentor engineering team members, conduct code reviews, and drive best practices in Python development.
Requirements
Requirements:
Degree in Computer Science or relevant experience in Software Development.
2-3+ years of software engineering experience specializing in Python backend development or data engineering.
Deep expertise in Python web frameworks (FastAPI) and data validation using Pydantic.
Solid proficiency in relational databases including PostgreSQL and MySQL, with strong SQL optimization skills.
Hands-on experience with data visualization tools and analytical applications using Plotly Dash.
Experience integrating machine learning models using Scikit-learn into production pipelines.
Strong automated testing discipline with Pytest.
Comfortable with Version Control Systems, specifically GitHub.
Proven track record of processing and managing large-scale data workflows.
Familiarity with Linux systems and basic scripting.
Understanding of best practices for system architecture, including high availability and security.