Case Study: Deputy boosts data accuracy and speed with dbt Cloud
Key results
The challenge
As Deputy grew, its data architecture became so convoluted that the team could no longer efficiently use it, with a series of Snowflake tasks and Airflow jobs moving raw data into an analytics layer. A lack of documentation meant broken dashboards could take half a day of work to fix, as analysts had to manually trace tables back to source. The complex workflow left the data team struggling to fulfill even simple requests.
The solution
Deputy migrated its transformations to dbt Cloud, using dbt's documentation and data-lineage features to see what was running under the hood of its reporting and to identify and cut unused pipelines. Modeling logic and testing became centralized, making the data stack easier to debug and extend.
“The business now feels very comfortable asking us quirky ad hoc questions, and because we have a model in place, it's easy for us to either build it or answer that query. Productivity is skyrocketing.”
HAHuss AfzalData Director, Deputy
The results, in context
After implementing dbt, Deputy's data team received a perfect NPS score of 100 from internal partners, and dashboards that used to take months now take about a week. By cutting pipelines it no longer needed, the team saved 20% on Snowflake computing costs, and problems that once took half a day now take at most a couple of hours to fix.