analytics-engineer

Model data and build dbt pipelines from staging to marts.

467|103|Updated Jan 13, 2026

Search Tags:#analytics #SQL #dbt #data-modeling #data-pipelines #semantic-layer

Ready to run

npx skills add https://github.com/borghei/Claude-Skills --skill analytics-engineer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: analytics-engineer
Source: https://github.com/borghei/Claude-Skills/tree/main/data-analytics/analytics-engineer
Command: npx skills add https://github.com/borghei/Claude-Skills --skill analytics-engineer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analytics teams often struggle to design scalable, maintainable analytics pipelines that produce trusted data for decision-making. This Skill provides a framework for data modeling, dbt development, semantic layer management, and documentation to accelerate accuracy and governance.

Core Features & Use Cases

Data modeling and schema design for scalable warehouses
dbt project architecture including staging, intermediate, and marts
Semantic layer design to enable consistent metrics and BI dashboards
Testing, documentation, and governance for analytics pipelines
Use case: Build a complete analytics stack from source data to BI dashboards with reproducible pipelines

Quick Start

Initialize and structure a dbt analytics project with standard models and tests, then integrate with your warehouse and BI tools.

Frequently Asked Questions about analytics-engineer

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

FAQPage Schema

How do I structure a dbt project for scalable data pipelines?▼

To structure dbt data pipelines for scalability, separate models into staging, intermediate, and marts layers. This architecture ensures clean transformations, manageable dependencies, and trusted analytics outputs.

What is a semantic layer in analytics engineering and when do I need it?▼

A semantic layer in analytics engineering defines consistent metrics for BI dashboards. You need it to govern data definitions, prevent metric mismatches across reports, and maintain trusted analytics pipelines.

How do I build an end-to-end analytics stack from source data to BI dashboards?▼

Building an end-to-end analytics stack involves modeling source data in SQL, developing dbt pipelines through staging to marts, and integrating a semantic layer for BI dashboards to ensure reproducible data flows.

Can I use SQL and dbt to manage data warehouse schema design?▼

Yes, you can use SQL and dbt to manage data warehouse schema design. This approach enables scalable data modeling, automated testing frameworks, and pipeline governance directly within your modern data stack.

What is the best way to add testing and documentation to analytics pipelines?▼

The best way to add testing and documentation to analytics pipelines is integrating dbt frameworks. This enforces data quality checks and auto-generates project docs, accelerating accuracy and governance.