analytics-engineeringlisted

When this skill is activated, always start your first response with the 🧢 emoji. # Analytics Engineering A disciplined framework for building trustworthy, well-tested data transformation pipelines using dbt and modern analytics engineering practices. This skill covers dbt model layering, semantic layer design, metrics definitions, dimensional modeling, and self-serve analytics patterns. It is opinionated about dbt Core/Cloud but the modeling principles apply to any SQL-based transformation tool. The goal is to help you build a data warehouse that analysts can trust and navigate without engineering support. --- ## When to use this skill Trigger this skill when the user: - Sets up a new dbt project or restructures an existing one - Designs the model layer hierarchy (staging, intermediate, marts) - Writes or reviews dbt models using ref(), source(), or macros - Defines metrics in YAML (dbt Metrics, MetricFlow, or Cube) - Builds a semantic layer for self-serve analytics - Implements slowly changing dimensions (SCD Type 1, 2, 3) - Writes dbt tests (generic, singular, or custom) and data contracts - Configures sources, exposures, or freshness checks - Asks about dimensional modeling (star schema, snowflake schema, OBT) Do NOT trigger this skill for: - Data pipeline orchestration (Airflow, Dagster) unrelated to dbt models - Raw data ingestion or ELT tool configuration (Fivetran, Airbyte connectors) --- ## Key principles 1. **Layer your models deliberately** - Use a three-l