For data engineers supporting revenue ops and growth, bad metrics create bottlenecks. StructuredBot tackles this by automating dbt reviews, aligning key metrics like CAC and MRR across teams, and eliminating the headaches of inconsistent reporting. With StructuredBot, your data pipeline becomes a source of reliable, actionable insights, allowing you to focus on scaling, not reconciling data.

Data is a critical asset for any growth-focused company. But as your team scales, keeping your dbt models clean, consistent, and optimized becomes harder. That’s where this GitHub app comes in. It automates dbt code reviews directly in your pull requests, so your data models stay reliable, reusable, and aligned with your key metrics.

Here’s how it works:

1. Automated dbt Reviews
   The app kicks in whenever a pull request is opened or updated. It instantly analyzes dbt models, flagging issues related to performance, structure, and adherence to best practices. This reduces the need for manual reviews and speeds up your development process.

2. Model Reuse Suggestions
   The app identifies opportunities to reuse existing models, promoting modularity and reducing redundancy. It suggests breaking down complex models and reusing components to keep your project scalable and maintainable.

3. Metric Consistency
   Misaligned metrics can lead to bad decisions. The app checks new models against your existing semantic layer, catching inconsistencies in metric definitions so you can trust the data across your business.

4. SQL Performance Optimization
   The app analyzes SQL within dbt models, detecting anti-patterns like unnecessary subqueries or full-table scans. It provides recommendations for improving query performance.

Automate the review process, save time, and keep your dbt models in top shape— so your team to focus on building to drive growth.