data-engineering-skills

🥈Silver

Data engineering skills for analytics and data engineers working with dbt and Snowflake. Automate data pipelines, transform data, and generate SQL code. Integrates with Claude Code for AI-assisted data workflows.

4920Updated 1w ago

Intermediate30min to implementautomation

Saves ~300 min per use

Quick InstallView Source

git clone https://github.com/AltimateAI/data-engineering-skills.git

Works with:

Claude

Overview

About This Skill

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/AltimateAI/data-engineering-skills

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

I'm a data engineer working with [COMPANY] in the [INDUSTRY] sector. I need help with [SPECIFIC DATA ENGINEERING TASK] using dbt and Snowflake. Here's the [DATA] I'm working with: [INSERT DATA]. Can you help me [GENERATE/DEBUG/OPITMIZE] the SQL code or data pipeline?

Example Output

# Data Pipeline Optimization for Retail Analytics

## Current Pipeline Issues
- Slow query performance on customer segmentation queries
- Redundant data transformations in the pipeline
- Inconsistent data quality checks

## Recommended Optimizations
1. **Materialized Views**: Implement materialized views for frequently accessed customer segments
2. **Incremental Models**: Convert full refresh models to incremental models where possible
3. **Data Quality Tests**: Add dbt tests for NULL values and data range validation
4. **Query Optimization**: Rewrite complex joins and use Snowflake's query profiling tools

## Estimated Performance Gains
- 30-40% reduction in query execution time
- 20% decrease in data pipeline runtime
- Improved data reliability and consistency