analytics-engineering

What problem does it solve?

Analytic teams struggle with inconsistent analytics engineering practices across dbt, semantic layers, and metrics definitions. This skill provides a standardized, production-grade approach to building trustworthy data transformations and self-serve analytics.

Core Features & Use Cases

• dbt layering guidance (staging, intermediate, marts) and renaming conventions
• Semantic layer design (entities, dimensions, measures, metrics)
• Metrics definition and governance via YAML
• Incremental modeling patterns and tests
• Self-serve analytics enablement with documentation and catalog patterns
• Use case: Build a robust analytics warehouse for analysts to query with confidence

Quick Start

Create a dbt project skeleton with staging, intermediate, and marts and define a semantic model for a simple orders table and a derived revenue metric.