Data Analytics Engineering

This one's built for the messy middle of the data stack: turning raw tables into reliable metrics that BI tools and business users can actually trust. It walks you through building transformation layers (dbt, SQLMesh), defining semantic models, and setting up data quality tests that catch breaks before dashboards do. The workflow is methodical: metric dictionary first, then staging to marts, then tests and docs. What's smart here is the trend awareness protocol. It forces live web searches before recommending tools because this space moves fast (semantic layers, metrics stores, the whole dbt vs SQLMesh thing). If you're tired of broken dashboards and metric drift, this gives you a governance-first playbook with templates for metric dictionaries and data contracts.