dbt-template

type	Skill
name	dbt-template
title	dbt Template
description	Simple dbt workflow patterns for data transformation, testing, and project management. Use when creating dbt projects, running data pipelines, or implementing analytics engineering workflows.
tags	["data","dbt","analytics","transformation","data-engineering"]
timestamp	"2026-06-14T00:00:00.000Z"
id	skill-dbt-template
cdr_ref	null
created	"2026-01-16T00:00:00.000Z"
modified	"2026-06-14T00:00:00.000Z"
verified	"2026-05-21T00:00:00.000Z"
age_days	149
evidence	[]
instruction_type	Generation
priority	Standard

dbt Template

What This Skill Provides

Simple dbt workflow patterns for data engineering projects with focus on:

• Basic dbt project setup and structure
• Simple dbt CLI commands (run, test, build)
• Integration with team constitution principles
• Reference patterns for analytics engineering

When to Use This Skill

• Creating new dbt projects
• Setting up data transformation workflows
• Implementing analytics engineering patterns
• Managing dbt project lifecycle

Quick Setup Guide

Prerequisites

• dbt Core installed (pip install dbt-core)
• Data warehouse connection configured
• Basic SQL knowledge

Initialize dbt Project

# Create new dbt project
dbt init my-analytics-project
cd my-analytics-project

# Configure connection in profiles.yml
# See references for team constitution guidelines

Core Patterns

Basic dbt Workflow

Rule: Use standard dbt workflow for data transformation

Implementation:

# Run models (data transformation)
dbt run

# Test models (data quality)
dbt test

# Build documentation
dbt docs generate

Project Structure

Rule: Follow standard dbt project structure

Implementation:

my-analytics-project/
├── models/          # SQL models for data transformation
├── tests/           # Data quality tests
├── snapshots/       # Snapshot configurations
├── macros/          # Reusable SQL macros
├── dbt_project.yml # Project configuration
└── profiles.yml     # Connection configurations

Integration with Team Constitution

Principle 2 (Build for Observability)

• Add logging to dbt models for debugging
• Include data quality metrics in tests
• Document model dependencies

Principle 4 (Tests Drive Confidence)

• Write tests for all critical models
• Include data freshness checks
• Test edge cases and data boundaries

Principle 9 (Simplicity First)

• Keep models focused and single-purpose
• Avoid complex nested transformations
• Use clear naming conventions

Principle 11 (Goal-Driven Execution)

• Define success criteria for data pipelines
• Measure data quality and performance
• Document business value of transformations

References

• Team Constitution: See references/constitution.md (Principles 2, 4, 9, 11)
• Testing Guidelines: See references/testing_guide.md for data quality patterns

Usage Examples

Simple Data Transformation

-- models/stg_customers.sql
SELECT 
    id,
    name,
    email,
    created_at
FROM source.raw_customers
WHERE created_at >= '2023-01-01'

Basic Data Test

-- tests/stg_customers_unique_email.sql
SELECT email FROM stg_customers
GROUP BY email
HAVING count(*) > 1

Best Practices

• Keep models simple and focused
• Use descriptive naming conventions
• Add documentation for complex transformations
• Test critical data quality assumptions
• Follow team security guidelines for data access