senior-data-engineer by davila7/claude-code-templates
npx skills add https://github.com/davila7/claude-code-templates --skill senior-data-engineer面向生产级 AI/ML/数据系统的世界级高级数据工程师技能。
# Core Tool 1
python scripts/pipeline_orchestrator.py --input data/ --output results/
# Core Tool 2
python scripts/data_quality_validator.py --target project/ --analyze
# Core Tool 3
python scripts/etl_performance_optimizer.py --config config.yaml --deploy
此技能涵盖以下世界级能力:
编程语言: Python, SQL, R, Scala, Go 机器学习框架: PyTorch, TensorFlow, Scikit-learn, XGBoost 数据工具: Spark, Airflow, dbt, Kafka, Databricks 大语言模型框架: LangChain, LlamaIndex, DSPy 部署: Docker, Kubernetes, AWS/GCP/Azure 监控: MLflow, Weights & Biases, Prometheus 数据库: PostgreSQL, BigQuery, Snowflake, Pinecone
完整指南位于 references/data_pipeline_architecture.md,涵盖:
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在这里展示您的产品或服务
触达数万 AI 开发者,精准高效
完整工作流文档位于 references/data_modeling_patterns.md,包括:
技术参考指南位于 references/dataops_best_practices.md,包含:
基于分布式计算的企业级数据处理:
具备高可用性的生产级机器学习系统:
高吞吐量推理系统:
延迟:
吞吐量:
可用性:
# Development
python -m pytest tests/ -v --cov
python -m black src/
python -m pylint src/
# Training
python scripts/train.py --config prod.yaml
python scripts/evaluate.py --model best.pth
# Deployment
docker build -t service:v1 .
kubectl apply -f k8s/
helm upgrade service ./charts/
# Monitoring
kubectl logs -f deployment/service
python scripts/health_check.py
references/data_pipeline_architecture.mdreferences/data_modeling_patterns.mdreferences/dataops_best_practices.mdscripts/ 目录作为世界级的高级专业人士:
每周安装量
752
代码仓库
GitHub 星标
23.4K
首次出现
Jan 20, 2026
安全审计
已安装于
opencode596
codex583
gemini-cli562
github-copilot512
cursor497
claude-code496
World-class senior data engineer skill for production-grade AI/ML/Data systems.
# Core Tool 1
python scripts/pipeline_orchestrator.py --input data/ --output results/
# Core Tool 2
python scripts/data_quality_validator.py --target project/ --analyze
# Core Tool 3
python scripts/etl_performance_optimizer.py --config config.yaml --deploy
This skill covers world-class capabilities in:
Languages: Python, SQL, R, Scala, Go ML Frameworks: PyTorch, TensorFlow, Scikit-learn, XGBoost Data Tools: Spark, Airflow, dbt, Kafka, Databricks LLM Frameworks: LangChain, LlamaIndex, DSPy Deployment: Docker, Kubernetes, AWS/GCP/Azure Monitoring: MLflow, Weights & Biases, Prometheus Databases: PostgreSQL, BigQuery, Snowflake, Pinecone
Comprehensive guide available in references/data_pipeline_architecture.md covering:
Complete workflow documentation in references/data_modeling_patterns.md including:
Technical reference guide in references/dataops_best_practices.md with:
Enterprise-scale data processing with distributed computing:
Production ML system with high availability:
High-throughput inference system:
Latency:
Throughput:
Availability:
# Development
python -m pytest tests/ -v --cov
python -m black src/
python -m pylint src/
# Training
python scripts/train.py --config prod.yaml
python scripts/evaluate.py --model best.pth
# Deployment
docker build -t service:v1 .
kubectl apply -f k8s/
helm upgrade service ./charts/
# Monitoring
kubectl logs -f deployment/service
python scripts/health_check.py
references/data_pipeline_architecture.mdreferences/data_modeling_patterns.mdreferences/dataops_best_practices.mdscripts/ directoryAs a world-class senior professional:
Technical Leadership
Strategic Thinking
Collaboration
Innovation
Production Excellence
Weekly Installs
752
Repository
GitHub Stars
23.4K
First Seen
Jan 20, 2026
Security Audits
Gen Agent Trust HubPassSocketPassSnykPass
Installed on
opencode596
codex583
gemini-cli562
github-copilot512
cursor497
claude-code496
Azure Data Explorer (Kusto) 查询技能:KQL数据分析、日志遥测与时间序列处理
98,500 周安装