Problem

Before the launch of our data quality toolkit, our team faced a major challenge in ensuring that all dbt code was verified after a pull request. This process was time-consuming and prone to errors, leading to delays in project timelines and decreased efficiency.

Solution

To address this problem, we developed a data quality toolkit that automatically verifies dbt code after a pull request. The toolkit includes a set of rules that check for common errors and inconsistencies, providing developers with immediate feedback on any issues that need to be addressed. This streamlined the verification process and allowed our team to focus on more important tasks.

Impact

Reduced verification time by X%
Improved overall project efficiency by X%
Decreased errors and inconsistencies in dbt code
Increase in developer satisfaction due to streamlined process

Team

Team Member #1 - Software Engineer
- Oversaw the development of the data quality toolkit
- Coded the set of rules for verifying dbt code after a pull request
Team Member #2 - Quality Assurance Analyst
- Tested the functionality of the data quality toolkit
- Created test cases for different scenarios and edge cases
Team Member #3 - Project Manager
- Oversaw the coordination of the product launch
- Maintained communication with stakeholders and team members

Automated Data Quality Verification Toolkit