How does it handle data modeling?

The skill provides guidance on Star and Snowflake schemas, SCD types 1-6, and Data Vault modeling to ensure efficient analytical querying and historical tracking.

Can it help migrate from Batch to Streaming?

Yes, it includes a decision framework to evaluate latency requirements and provides implementation patterns for Kafka and Spark Structured Streaming.

How does this skill help with pipeline orchestration?

It provides templates and scripts for generating Airflow, Prefect, or Dagster configurations, including scheduling, source-to-destination mapping, and error handling logic.

Does it support modern data stack tools?

Absolutely, it includes deep expertise in dbt for transformations, Snowflake/BigQuery for warehousing, and Great Expectations for data quality.

Can it optimize existing SQL queries?

Yes, it analyzes query patterns and provides specific recommendations for indexing, partitioning, and clustering to reduce execution time and compute costs.

Senior Data Engineer

Name: Senior Data Engineer
Author: zhangzhang-111-i

byzhangzhang-111-i

0•

데이터베이스 관리

Architects and builds production-grade data pipelines, ETL/ELT systems, and scalable data infrastructure using modern tech stacks.

This skill transforms Claude into a senior data architect capable of designing and implementing complex data ecosystems. It provides expert guidance on batch and streaming architectures (Lambda/Kappa), dimensional data modeling, and pipeline orchestration using tools like Airflow, dbt, and Spark. Whether you are optimizing slow SQL queries, setting up data quality frameworks, or choosing between a Data Warehouse and a Lakehouse, this skill ensures your data infrastructure follows industry-standard DataOps best practices for reliability and scalability.

주요 기능

01Advanced data modeling for Star Schemas, Snowflake, and Data Vaults

02Architecture decision framework for Batch vs. Streaming and Lambda vs. Kappa

03Performance tuning for Spark jobs, SQL queries, and Airflow DAGs

04Built-in data quality validation and freshness monitoring patterns

050 GitHub stars

06Automated ETL/ELT pipeline generation and orchestration configuration

사용 사례

01Implementing real-time event streaming pipelines with Kafka and Flink

02Designing a scalable data lakehouse architecture using Delta Lake or Iceberg

03Automating data quality checks and schema validation for production pipelines

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add zhangzhang-111-i/claude-skills111 senior-data-engineer

For use in Claude.ai and ChatGPT

주요 기능

01Advanced data modeling for Star Schemas, Snowflake, and Data Vaults

02Architecture decision framework for Batch vs. Streaming and Lambda vs. Kappa

03Performance tuning for Spark jobs, SQL queries, and Airflow DAGs

04Built-in data quality validation and freshness monitoring patterns

050 GitHub stars

06Automated ETL/ELT pipeline generation and orchestration configuration

사용 사례

01Implementing real-time event streaming pipelines with Kafka and Flink

02Designing a scalable data lakehouse architecture using Delta Lake or Iceberg

03Automating data quality checks and schema validation for production pipelines

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add zhangzhang-111-i/claude-skills111 senior-data-engineer

For use in Claude.ai and ChatGPT