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analytics-engineering

Standardize dbt model layering and semantic layer definitions with YAML metrics.

207|31|Updated Mar 14, 2026
Search Tags:#analytics#metrics#dbt#modeling#data-warehouse#semantic-layer#self-serve
One-click install
npx skills add https://github.com/AbsolutelySkilled/AbsolutelySkilled --skill analytics-engineering
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: analytics-engineering
Source: https://github.com/AbsolutelySkilled/AbsolutelySkilled/tree/main/skills/analytics-engineering
Command: npx skills add https://github.com/AbsolutelySkilled/AbsolutelySkilled --skill analytics-engineering

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

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.

Frequently Asked Questions about analytics-engineering

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I structure dbt models for a production-grade data warehouse?▼

Structure dbt models using staging, intermediate, and marts layers. This standardization enforces consistent naming conventions and data contracts, ensuring trustworthy transformations for your analytics warehouse.

What is the best way to define metrics and semantic models in dbt?▼

Define metrics and semantic models using YAML-based configurations. This approach standardizes entities, dimensions, and measures, enabling governed metric definitions and reliable self-serve analytics for analysts.

Can I use this analytics engineering approach with MetricFlow and Cube?▼

Yes, this approach supports semantic layer design and metrics governance using tools like MetricFlow and Cube. It provides guidelines for defining entities, dimensions, and measures across these platforms.

How do I enable self-serve analytics for my team using dbt?▼

Enable self-serve analytics by enforcing frontmatter metadata, standardizing dbt model layering, and defining semantic models. This provides catalog patterns and documentation for analysts to query data with confidence.

What patterns should I use for incremental dbt models and testing?▼

Use standardized incremental modeling patterns and tests provided by this approach. It enforces frontmatter metadata and data contracts to maintain reliable, production-grade data warehouse transformations.

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