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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/alirezarezvani/claude-skills --skill senior-data-engineer- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads senior-data-engineer automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Senior Data Engineer
Production-grade data engineering skill for building scalable, reliable data systems.
Table of Contents
- Trigger Phrases
- Quick Start
- Workflows
- Architecture Decision Framework
- Tech Stack
- Reference Documentation
- 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"
Quick Start
Core Tools
# Generate pipeline orchestration config
python scripts/pipeline_orchestrator.py generate \
--type airflow \
--source postgres \
--destination snowflake \
--schedule "0 5 * * *"
# Validate data quality
python scripts/data_quality_validator.py validate \
--input data/sales.parquet \
--schema schemas/sales.json \
--checks freshness,completeness,uniqueness
# Optimize ETL performance
python scripts/etl_performance_optimizer.py analyze \
--query queries/daily_aggregation.sql \
--engine spark \
--recommend
Workflows
→ See references/workflows.md for details
Architecture Decision Framework
Use this framework to choose the right approach for your data pipeline.
Batch vs Streaming
| Criteria | Batch | Streaming |
|---|---|---|
| Latency requirement | Hours to days | Seconds to minutes |
| Data volume | Large historical datasets | Continuous event streams |
| Processing complexity | Complex transformations, ML | Simple aggregations, filtering |
| Cost sensitivity | More cost-effective | Higher infrastructure cost |
| Error handling | Easier to reprocess | Requires careful design |
Decision Tree:
Is real-time insight required?
├── Yes → Use streaming
│ └── Is exactly-once semantics needed?
│ ├── Yes → Kafka + Flink/Spark Structured Streaming
│ └── No → Kafka + consumer groups
└── No → Use batch
└── Is data volume > 1TB daily?
├── Yes → Spark/Databricks
└── No → dbt + warehouse compute
Lambda vs Kappa Architecture
| Aspect | Lambda | Kappa |
|---|---|---|
| Complexity | Two codebases (batch + stream) | Single codebase |
| Maintenance | Higher (sync batch/stream logic) | Lower |
| Reprocessing | Native batch layer | Replay from source |
| Use case | ML training + real-time serving | Pure event-driven |
When to choose Lambda:
- Need to train ML models on historical data
- Complex batch transformations not feasible in streaming
- Existing batch infrastructure
When to choose Kappa:
- Event-sourced architecture
- All processing can be expressed as stream operations
- Starting fresh without legacy systems
Data Warehouse vs Data Lakehouse
| Feature | Warehouse (Snowflake/BigQuery) | Lakehouse (Delta/Iceberg) |
|---|---|---|
| Best for | BI, SQL analytics | ML, unstructured data |
| Storage cost | Higher (proprietary format) | Lower (open formats) |
| Flexibility | Schema-on-write | Schema-on-read |
| Performance | Excellent for SQL | Good, improving |
| Ecosystem | Mature BI tools | Growing ML tooling |
Tech Stack
| Category | Technologies |
|---|---|
| Languages | Python, SQL, Scala |
| Orchestration | Airflow, Prefect, Dagster |
| Transformation | dbt, Spark, Flink |
| Streaming | Kafka, Kinesis, Pub/Sub |
| Storage | S3, GCS, Delta Lake, Iceberg |
| Warehouses | Snowflake, BigQuery, Redshift, Databricks |
| Quality | Great Expectations, dbt tests, Monte Carlo |
| Monitoring | Prometheus, Grafana, Datadog |
Reference Documentation
1. Data Pipeline Architecture
See references/data_pipeline_architecture.md for:
- Lambda vs Kappa architecture patterns
- Batch processing with Spark and Airflow
- Stream processing with Kafka and Flink
- Exactly-once semantics implementation
- Error handling and dead letter queues
2. Data Modeling Patterns
See references/data_modeling_patterns.md for:
- Dimensional modeling (Star/Snowflake)
- Slowly Changing Dimensions (SCD Types 1-6)
- Data Vault modeling
- dbt best practices
- Partitioning and clustering
3. DataOps Best Practices
See references/dataops_best_practices.md for:
- Data testing frameworks
- Data contracts and schema validation
- CI/CD for data pipelines
- Observability and lineage
- Incident response
Troubleshooting
→ See references/troubleshooting.md for details
FAQ
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 senior-data-engineer?
Run npx skills add https://github.com/alirezarezvani/claude-skills --skill senior-data-engineer in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/senior-data-engineer/ (personal) or .claude/skills/senior-data-engineer/ (project).
Which AI agents can use senior-data-engineer?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is senior-data-engineer safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains senior-data-engineer?
It is published by alirezarezvani in alirezarezvani/claude-skills. It has 1,114 recorded installs.
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