---
name: data-pipeline
description: Data engineering lifecycle for ETL pipelines, data warehouses, and analytics workflows
stages: [discovery, extraction, transformation, validation, deployment]
category: engineering
default_model: sonnet
---

# Data Pipeline Studio

Lifecycle for data engineering work: ETL/ELT pipelines, data warehouse modeling,
analytics workflows, and data integration projects. Use this studio when the intent
involves moving, reshaping, or validating data across systems.

Appropriate for:
- Building new ETL/ELT pipelines from source to target
- Data warehouse or lakehouse schema design and implementation
- Analytics pipeline development (batch or streaming)
- Data migration between systems or schema versions
- Data quality framework implementation
