senior-data-engineer

SKILL.md

---
name: senior-data-engineer
description: World-class 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, or implementing data governance.
---

# Senior Data Engineer

World-class senior data engineer skill for production-grade AI/ML/Data systems.

## Quick Start

### Main Capabilities

```bash
# 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
```

## Core Expertise

This skill covers world-class capabilities in:

- Advanced production patterns and architectures
- Scalable system design and implementation
- Performance optimization at scale
- MLOps and DataOps best practices
- Real-time processing and inference
- Distributed computing frameworks
- Model deployment and monitoring
- Security and compliance
- Cost optimization
- Team leadership and mentoring

## Tech Stack

**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

## Reference Documentation

### 1. Data Pipeline Architecture

Comprehensive guide available in `references/data_pipeline_architecture.md` covering:

- Advanced patterns and best practices
- Production implementation strategies
- Performance optimization techniques
- Scalability considerations
- Security and compliance
- Real-world case studies

### 2. Data Modeling Patterns

Complete workflow documentation in `references/data_modeling_patterns.md` including:

- Step-by-step processes
- Architecture design patterns
- Tool integration guides
- Performance tuning strategies
- Troubleshooting procedures

### 3. Dataops Best Practices

Technical reference guide in `references/dataops_best_practices.md` with:

- System design principles
- Implementation examples
- Configuration best practices
- Deployment strategies
- Monitoring and observability

## Production Patterns

### Pattern 1: Scalable Data Processing

Enterprise-scale data processing with distributed computing:

- Horizontal scaling architecture
- Fault-tolerant design
- Real-time and batch processing
- Data quality validation
- Performance monitoring

### Pattern 2: ML Model Deployment

Production ML system with high availability:

- Model serving with low latency
- A/B testing infrastructure
- Feature store integration
- Model monitoring and drift detection
- Automated retraining pipelines

### Pattern 3: Real-Time Inference

High-throughput inference system:

- Batching and caching strategies
- Load balancing
- Auto-scaling
- Latency optimization
- Cost optimization

## Best Practices

### Development

- Test-driven development
- Code reviews and pair programming
- Documentation as code
- Version control everything
- Continuous integration

### Production

- Monitor everything critical
- Automate deployments
- Feature flags for releases
- Canary deployments
- Comprehensive logging

### Team Leadership

- Mentor junior engineers
- Drive technical decisions
- Establish coding standards
- Foster learning culture
- Cross-functional collaboration

## Performance Targets

**Latency:**
- P50: < 50ms
- P95: < 100ms
- P99: < 200ms

**Throughput:**
- Requests/second: > 1000
- Concurrent users: > 10,000

**Availability:**
- Uptime: 99.9%
- Error rate: < 0.1%

## Security & Compliance

- Authentication & authorization
- Data encryption (at rest & in transit)
- PII handling and anonymization
- GDPR/CCPA compliance
- Regular security audits
- Vulnerability management

## Common Commands

```bash
# 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
```

## Resources

- Advanced Patterns: `references/data_pipeline_architecture.md`
- Implementation Guide: `references/data_modeling_patterns.md`
- Technical Reference: `references/dataops_best_practices.md`
- Automation Scripts: `scripts/` directory

## Senior-Level Responsibilities

As a world-class senior professional:

1. **Technical Leadership**
   - Drive architectural decisions
   - Mentor team members
   - Establish best practices
   - Ensure code quality

2. **Strategic Thinking**
   - Align with business goals
   - Evaluate trade-offs
   - Plan for scale
   - Manage technical debt

3. **Collaboration**
   - Work across teams
   - Communicate effectively
   - Build consensus
   - Share knowledge

4. **Innovation**
   - Stay current with research
   - Experiment with new approaches
   - Contribute to community
   - Drive continuous improvement

5. **Production Excellence**
   - Ensure high availability
   - Monitor proactively
   - Optimize performance
   - Respond to incidents