Case Study: Leading Global Investment Bank achieves scalable financial data pipelines with Sigmoid

Building robust data pipelines for a leading investment bank to make quality datasets ready for ML use cases

Leading Global Investment Bank needed a scalable way to collect financial data from multiple global providers such as Bloomberg and Refinitiv, each with different file formats, fields, and data types, and consolidate it into a single repository. Sigmoid worked with the customer’s proprietary IDE and in-house pipeline system to map and transform the data into a common format and support continuous loading at regular intervals.

Sigmoid built over 400 automated data pipelines to ingest data from more than 100 financial data providers into a central database, with config-driven controls for source and sink connections, scheduled runs, and QA deployment. The solution improved onboarding speed for new providers, enabled continuous monitoring and debugging, and delivered 2X efficiency in identifying and fixing pipeline issues while ensuring high data quality.