# Data Engineering

Data engineering methodology — database operations (vector, relational, graph, time-series), ETL/ELT pipeline design (dbt patterns, incremental loading), SQL analytical patterns, data quality monitoring, schema migration, and storage infrastructure management. Grounded in operational patterns for production data systems.

## Why Install This Skill

Your agent gets operational patterns for production data systems — real SQL, dbt models, backup commands, and migration strategies instead of textbook theory.

## What You Get

| Directory | Purpose |
|-----------|---------|
| `SKILL.md` | Core methodology, trigger conditions, reference index |
| `references/` | Deep-dive reference files loaded on demand |

## Triggers

Designing ETL/ELT pipelines, writing analytical SQL, operating vector/graph/time-series databases, planning migrations, or setting up data quality monitoring.

## Requirements

Platform-agnostic. References cover PostgreSQL, DuckDB, ClickHouse, BigQuery, Snowflake, Neo4j, InfluxDB, TimescaleDB, and dbt.

## Quick Start

Load SKILL.md for the methodology overview and reference table, then load specific references as needed for the task at hand.