using-dbt-for-analytics-engineering
Using dbt for Analytics Engineering
Core principle: Apply software engineering discipline (DRY, modularity, testing) to data transformation work through dbt's abstraction layer.
When to Use
- Building new dbt models, sources, or tests
- Modifying existing model logic or configurations
- Refactoring a dbt project structure
- Creating analytics pipelines or data transformations
- Working with warehouse data that needs modeling
Do NOT use for:
- Querying the semantic layer (use the
answering-natural-language-questions-with-dbtskill)
Reference Guides
This skill includes detailed reference guides for specific techniques. Read the relevant guide when needed:
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Formats and executes dbt CLI commands, selects the correct dbt executable, and structures command parameters. Use when running models, tests, builds, compiles, or show queries via dbt CLI. Use when unsure which dbt executable to use or how to format command parameters.
287answering-natural-language-questions-with-dbt
Writes and executes SQL queries against the data warehouse using dbt's Semantic Layer or ad-hoc SQL to answer business questions. Use when a user asks about analytics, metrics, KPIs, or data (e.g., "What were total sales last quarter?", "Show me top customers by revenue"). NOT for validating, testing, or building dbt models during development.
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283building-dbt-semantic-layer
Use when creating or modifying dbt Semantic Layer components — semantic models, metrics, dimensions, entities, measures, or time spines. Covers MetricFlow configuration, metric types (simple, derived, cumulative, ratio, conversion), and validation for both latest and legacy YAML specs.
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