What data stacks does this skill support?

It is optimized for SQL (MySQL, Data Warehouses), Kafka streaming, Python/Java pipeline code, and orchestrators like Temporal or cron.

How does it handle data quality?

The skill follows a strict checklist including input validation, duplicate detection, error record capturing, and automated audit logging to ensure data integrity.

Can it help with cloud data warehouse costs?

Yes, it includes a financial integration protocol to consult on sizing and resource decisions for warehouses and third-party tools before implementation.

Is it compatible with my existing infrastructure?

Absolutely. It features a customization layer where you can define your specific technical stack, data domains, and repository structure.

Data Engineer Agent

Name: Data Engineer Agent
Author: violetio

byvioletio

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데이터베이스 관리

Builds and maintains robust data pipelines, analytics infrastructure, and ETL processes with high data quality standards.

소개

The Data Engineer skill transforms Claude into a specialized authority on data infrastructure and analytics. It automates the design and implementation of idempotent ETL pipelines, manages data warehouse architectures, and integrates external data sources while ensuring strict data quality and validation. Whether you are scaling Kafka event streaming or optimizing complex SQL queries, this skill provides a structured framework for building resilient, production-ready data systems with clear lineage and performance monitoring.

주요 기능

Comprehensive status reporting and lineage documentation
End-to-end ETL and data pipeline orchestration
1 GitHub stars
Kafka-based event streaming and integration patterns
Data warehouse architecture and query optimization
Automated data validation and quality check implementation

사용 사례

Implementing automated data quality monitoring and error recovery workflows
Designing idempotent data pipelines for reliable analytics reporting
Building integrations between disparate external data sources and central data warehouses

소개

주요 기능

Comprehensive status reporting and lineage documentation
End-to-end ETL and data pipeline orchestration
1 GitHub stars
Kafka-based event streaming and integration patterns
Data warehouse architecture and query optimization
Automated data validation and quality check implementation

사용 사례

Implementing automated data quality monitoring and error recovery workflows
Designing idempotent data pipelines for reliable analytics reporting
Building integrations between disparate external data sources and central data warehouses