# Data Engineering

> Design and implement data pipelines, ETL processes, and data infrastructure. Use when building data ingestion, transformation, or storage systems.

- **Type:** Skill
- **Install:** `agentstack add skill-ihatesea69-kiro-kit-data-engineering`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [ihatesea69](https://agentstack.voostack.com/s/ihatesea69)
- **Installs:** 0
- **Category:** [Data & Analytics](https://agentstack.voostack.com/c/data-and-analytics)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [ihatesea69](https://github.com/ihatesea69)
- **Source:** https://github.com/ihatesea69/kiro-kit/tree/main/.kiro/skills/data-engineering
- **Website:** https://www.npmjs.com/package/kiro-kit

## Install

```sh
agentstack add skill-ihatesea69-kiro-kit-data-engineering
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Data Engineering

Activate this skill when designing data pipelines or working with data infrastructure.

## When to Use

- Building ETL/ELT pipelines
- Designing data warehouse schemas
- Implementing streaming data processing
- Optimizing data storage and retrieval
- Setting up data quality checks

## Core Tools

- **Apache Airflow**: Workflow orchestration
- **dbt**: SQL-based transformations
- **Apache Spark/PySpark**: Distributed processing
- **DVC**: Data version control
- **Great Expectations**: Data validation

## Patterns

```python
# Airflow DAG pattern
from airflow import DAG
from airflow.operators.python import PythonOperator

with DAG("etl_pipeline", schedule="@daily") as dag:
    extract = PythonOperator(task_id="extract", python_callable=extract_fn)
    transform = PythonOperator(task_id="transform", python_callable=transform_fn)
    load = PythonOperator(task_id="load", python_callable=load_fn)
    extract >> transform >> load
```

## Rules

- Idempotent operations (safe to re-run)
- Schema validation at pipeline boundaries
- Incremental processing over full reloads when possible
- Monitor data freshness and quality metrics
- Version control data schemas alongside code

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [ihatesea69](https://github.com/ihatesea69)
- **Source:** [ihatesea69/kiro-kit](https://github.com/ihatesea69/kiro-kit)
- **License:** MIT
- **Homepage:** https://www.npmjs.com/package/kiro-kit

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-ihatesea69-kiro-kit-data-engineering
- Seller: https://agentstack.voostack.com/s/ihatesea69
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.