Case Study: Blend achieves faster, more reliable data operations with dbt Cloud and Monte Carlo

Blend scales the impact of reliable data with dbt Cloud and Monte Carlo

Blend, a banking and financial services software company, needed a more reliable and scalable way to manage growing data demands across dozens of sources and teams. As its data became central to both internal reporting and customer-facing products, manual SQL workflows in Airflow and an in-house data quality POC were creating bottlenecks, consuming engineering time, and straining warehouse resources. dbt Labs helped Blend modernize its data foundation with dbt Cloud and Monte Carlo.

With dbt Labs’ dbt Cloud, Blend enabled modular SQL development and self-serve workflows for analysts, while Monte Carlo provided automated end-to-end data observability across production tables. The result was broader data quality coverage, faster incident detection, reduced compute costs, and improved trust in data for internal and external users. Blend also cut time-to-value by 4 months compared with its internal POC framework, while improving speed to insights and supporting reliable, customer-facing data products.

Blend

Albert Pan

Software Engineer