Python Backend Developer with knowledge in Postmaster, NodeJs, Volta, Docker, Azure Devops, Git, GitHub & Development.

I’m putting together a Python-driven backend whose core purpose is fast, reliable data processing. Although we will decide later exactly which domain the data belongs to, the system has to be flexible enough to accept any format we throw at it and expose the results through a clean Web API once the specification is locked down.

The service will read and write to traditional databases, consume and publish to third-party APIs, and move files around local or cloud file systems. I picture an architecture that leans on modern Python tooling—think FastAPI or Django REST for the eventual endpoints, SQLAlchemy or an ORM of your choice for database access, plus the usual suspects (Pandas, asyncio, Celery, Docker, GitHub Actions) for processing, concurrency and CI/CD. If you have a different stack that achieves the same goals, I’m open to hearing it.

What I need from you is straightforward:

• Lay out a scalable project structure and pick the right libraries.
• Build the core processing pipeline with clear, well-documented modules and tests.
• Provide connection layers to databases, external APIs and file storage that can be swapped out through configuration.
• Leave concise README and inline comments so other contributors can ramp up quickly.

Clean, idiomatic Python, meaningful commit messages and a short feedback loop are a must. If this sounds like your kind of project, tell me how you’d approach the data processing engine and which tools you’d reach for first.