Building payment pipelines with Python
After a decade away from hands-on coding, diving back into Python for payment processing has been both challenging and invigorating. At Mercury, we needed robust pipelines for handling 1099 interest payouts, and Python's ecosystem proved perfect for the job.
Why Python for Payments?
Python's data processing libraries-Pandas, NumPy, and the async capabilities of FastAPI-make it ideal for financial data transformations. The type hints in modern Python also help catch errors before they hit production, which is critical when dealing with money.
The Pipeline Architecture
We built an event-driven system using Python's asyncio and message queues. Each payment goes through validation, transformation, and reconciliation stages. The key was making each stage idempotent and traceable.
"When dealing with financial transactions, every operation must be reversible and auditable."
Lessons Learned
The biggest challenge wasn't the code itself, but ensuring compliance and audit trails. Python's logging and monitoring libraries made this manageable, but it required discipline in how we structured the code.