senior-data-engineer
Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.
Profile →npx skills add alirezarezvani/claude-skills --skill senior-data-engineer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Senior Data Engineer Production-grade data engineering skill for building scalable, reliable data systems. ## Table of Contents 1. [Trigger Phrases](#trigger-phrases) 2. [Quick Start](#quick-start) 3. [Workflows](#workflows) 4. [Architecture Decision Framework](#architecture-decision-framework) 5. [Tech Stack](#tech-stack) 6. [Reference Documentation](#reference-documentation) 7. [Troubleshooting](#troubleshooting) --- ## Trigger Phrases Activate this skill when you see: **Pipeline Design:** - "Design a data pipeline for..." - "Build an ETL/ELT process..." - "How should I ingest data from..." - "Set up data extraction from..." **Architecture:** - "Should I use batch or streaming?" - "Lambda vs Kappa architecture" - "How to handle late-arriving data" - "Design a data lakehouse" **Data Modeling:** - "Create a dimensional model..." - "Star schema vs snowflake" - "Implement slowly changing dimensions" - "Design a data vault" **Data Quality:** - "Add data validation to..." - "Set up data quality checks" - "Monitor data freshness" - "Implement data contracts" **Performance:** - "Optimize this Spark job" - "Query is running slow" - "Reduce pipeline execution time" - "Tune Airflow DAG" -
- Table of Contents
- Trigger Phrases
- Quick Start
- Core Tools
- Workflows
- Architecture Decision Framework
- Batch vs Streaming
- Lambda vs Kappa Architecture
- Data Warehouse vs Data Lakehouse
- Tech Stack
- Reference Documentation
- 1. Data Pipeline Architecture
- 2. Data Modeling Patterns
- 3. DataOps Best Practices
Generate pipeline orchestration config python scripts/pipeline_orchestrator.py generate \ Validate data quality python scripts/data_quality_validator.py validate \ Optimize ETL performance python scripts/etl_performance_optimizer.py analyze \
What does the senior-data-engineer skill do?
Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.
How do I install it?
Run `npx skills add alirezarezvani/claude-skills --skill senior-data-engineer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From alirezarezvani/claude-skills, a repository with 23,791 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.