Python
Python is not just a language, it is an ecosystem. From data pipelines and machine learning to web APIs and automation, Python’s readability, versatility, and thriving community make it the ideal choice for building reliable, maintainable software at every scale.
Why We Build Everything in Python
We chose Python as our primary language because it hits the sweet spot between developer productivity and raw capability. The same language that lets you prototype a machine learning idea in an afternoon also powers Instagram, Spotify’s data platform, and NASA’s scientific computing infrastructure.
- Expressive syntax, Python code reads like pseudocode, which means faster development, fewer bugs, and easier onboarding for new team members.
- Unmatched ecosystem, NumPy, Pandas, Scikit-learn, SQLAlchemy, Celery, Pydantic, the Python package index has a best-in-class library for almost every problem you will face.
- Async support, Python’s asyncio and frameworks like FastAPI allow building high-throughput I/O-bound services capable of handling thousands of concurrent connections.
- Type system, Modern Python with type hints, dataclasses, and Pydantic models provides the safety of a typed language while retaining Python’s flexibility.
- Interoperability, Python integrates seamlessly with C/C++ extensions, Rust via PyO3, R, Java, and virtually any database, message queue, or cloud service.
What We Build With Python
- REST and GraphQL APIs (FastAPI, Django REST Framework, Flask)
- Data engineering and ETL pipelines (Airflow, Prefect, dbt)
- Machine learning and MLOps systems (PyTorch, Scikit-learn, MLflow)
- DevOps automation and infrastructure tooling
- Web scraping and data collection frameworks
- Background task workers and job schedulers (Celery, RQ)
Our Python Standards
We enforce consistent code quality across every Python project: Black for formatting, Ruff or Flake8 for linting, mypy for static type checking, and pytest for testing. Every codebase we hand over meets these standards and includes a pyproject.toml that makes it trivial for any Python developer to get the environment running in minutes.