Custom API Ingestion Pipeline
Cut third-party financial data costs by 80% by replacing a vendor dependency with a purpose-built Python ingestion layer — designed, built, and shipped with a team of four.
Technologies Used
Key Features
Project Overview
Root Labs was spending the majority of its data budget on a third-party financial data vendor. The API was reliable but expensive, and the contract terms limited how the data could be used internally. The goal: build a replacement pipeline that gave us direct ownership of the data, at a fraction of the cost.
Technical Implementation
The solution was a Python-based ingestion layer built on top of direct API integrations with the underlying data providers. Each source was wrapped in a standardized adapter pattern, normalizing payloads into a consistent schema before loading into PostgreSQL via SQLAlchemy ORM.
Error handling was a first-class concern: exponential backoff on rate limits, dead-letter queues for failed records, and Slack alerting for anomalies above a configurable threshold. The full stack ran in Docker, making local development and deployment consistent.
Team & Outcome
This was a team project — I led a group of four engineers from initial design through production deployment. The 80% cost reduction came primarily from eliminating the vendor markup on raw API data, with secondary savings from reduced data volume through smarter filtering at ingestion time.