Python Financial Data Automation Pipeline

I am building an end-to-end Python solution that automatically ingests, processes, and reports on financial data pulled from CSV files, SQL databases, and live APIs. Clean, well-structured, and easily maintainable code is essential because the system will be extended over time.

Scope of work
You will take the project from initial design through deployment. That includes setting up the data-collection pipeline, writing transformation and analysis modules, implementing robust error handling and logging, building unit / integration tests, and preparing a production-ready deployment script or container. The stack is entirely Python, so I expect idiomatic use of tools such as pandas, SQLAlchemy, asyncio, and, where it adds value, lightweight AI/ML components for anomaly detection or forecasting.

Key expectations
• Development, debugging, and refactoring must follow PEP-8 and modern best practices.
• Automated test coverage should be high enough to protect against future regressions.
• The final package must run with a single command on a clean machine (Docker or similar).
• Documentation: clear docstrings, a concise README, and inline comments where logic is non-trivial.
• A realistic timeline broken into milestones with an accompanying cost estimate before we begin.

Proposed milestones
1. Architecture & data-source connectors
2. Core automation logic and transformations
3. Testing suite and CI setup
4. Deployment packaging and hand-over

Please confirm your availability, suggest any additional milestones you feel are necessary, and attach brief examples of similar automation or data projects you have led. I’m ready to move forward as soon as we finalise the schedule and cost.