Senior Software Engineer, Python
New
C
ComboCurveEnergy software
Source API remote eligibility restrictions: United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- DockerPythonFlaskGCPMongoDBFastAPIPandasRESTful APIs
Requirements
- Use production-grade Python 3.13+ with type annotations and async/await.
- Design REST or RPC services with OpenAPI contracts and evolve APIs without breaking consumers.
- Have hands-on experience building production services with Flask and/or FastAPI and configuring Gunicorn for WSGI deployment.
- Apply SOLID principles and clean architecture to design decoupled, maintainable services.
- Use Python tools such as pandas or numpy for statistical and exploratory analysis of structured datasets.
- Process medium-to-large datasets efficiently and communicate findings through simple visualizations or reports.
- Have experience taking features through the full lifecycle in cloud-based SaaS products, from development to deployment and monitoring.
- Use MongoDB in production, including schema design, indexing, and aggregation pipelines; experience with MongoEngine or PyMongo is relevant.
- Use uv or a similar tool for dependency management and virtual environments.
- Build comprehensive pytest suites using fixtures, parameterization, and mocked external services.
- Use Docker and Docker Compose for local and production environments and keep images lean.
- Apply code-quality and static-analysis tools such as ruff and pyright.
Responsibilities
- Write efficient Python code for structured time-series datasets that scales across cloud infrastructure.
- Own features from scoping and design through implementation, deployment, and monitoring.
- Contribute to software and infrastructure design discussions and architectural decisions.
- Build and maintain reliable, well-tested Python backend services and APIs.
- Model, query, and optimize MongoDB data using schema design, indexing, and aggregation pipelines.
- Deploy and operate containerized services on cloud infrastructure, including GCP components.
- Use AI tooling to accelerate delivery, improve code quality, and explore product capabilities.
- Participate in code reviews and create technical documentation and shared engineering standards.
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