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

Python SQL REST APIs PostgreSQL Docker

Key Features

Replaced a costly third-party data vendor with direct API integrations

80% reduction in monthly financial data costs

ORM-based pipeline with schema validation and automated retry logic

Built and mentored a 4-person engineering team through the full delivery cycle

Modular design supporting multiple data source adapters

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.

Completed on: Jun 1, 2024