Python SQL ORM Data Pipeline
Led a 4-person engineering team building a Python SQLAlchemy pipeline that automated financial data ingestion, validation, and storage — delivering clean, queryable data at scale.
Technologies Used
Key Features
Project Overview
Root Labs needed a robust internal data pipeline that could ingest financial data from multiple sources, validate it against a known schema, and store it in a queryable format — without relying on costly third-party vendors. I led a team of four engineers to design and build this system from scratch.
Technical Implementation
The pipeline was built in Python using SQLAlchemy as the ORM layer, connecting to a PostgreSQL database. Each data source was wrapped in a standardized adapter that normalized incoming payloads before they hit the validation layer. Validation checked for schema conformance, value ranges, and referential integrity — failed records were routed to a dead-letter queue for review rather than silently dropped.
The architecture was designed to be modular from day one: adding a new data source meant implementing one adapter class, not touching the core pipeline. This made it straightforward to onboard new providers as the business grew.
Team & Outcome
I owned the architecture and led the team through design, implementation, and deployment. The pipeline became the backbone of Root Labs’ internal data infrastructure — reliable, testable, and maintainable by the full team.