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Senior data engineer

Skill payals/ai-skills-engine/dot_cursor/skills/development/senior-data-engineer

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.From its SKILL.md

Install
npx -y skills add payals/ai-skills-engine --skill senior-data-engineer

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  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

5.4 KB, ~1.1k tokens by cl100k_base, as published. Nobody here has run it

Senior Data Engineer

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

Quick Start

Main Capabilities

# 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

# 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

What ships with it: 6 files

12.4 KB alongside SKILL.md, 3 of them executable

references/

scripts/

Gives 0 of the 12 instructions most data pipelines skills give in ~1.1k tokens

Counted across 149 of the 156 authors here whose files we hold, read 2026-09-06

  • Run a safe catch-up or sample benchmarkin 13 of 149, across 4 files
  • Rerun final accounting after the codified path executesin 13 of 149, across 4 files
  • Move compute to where the data already isin 13 of 149, across 4 files
  • Batch small files, requests, and writesin 13 of 149, across 4 files
  • Use manifests or checkpoints to skip completed filesin 13 of 149, across 4 files
  • Measure backlog across files, rows, and timestampsin 13 of 149, across 4 files
  • Codify the path as a CLI or scheduled jobin 12 of 149, across 3 files
  • Promote only the fastest correctness-preserving pathin 10 of 149, across 3 files
  • Separate the bottleneck categories before optimizingin 10 of 149, across 3 files
  • Prefer warehouse-native scans, joins, and appendsin 9 of 149, across 2 files
  • Make writes idempotent through keys, manifests, or replaceable stagingin 9 of 149, across 2 files
  • Retry failures with exponential backoffin 9 of 149, across 7 files

Said here and by no other author read

  • Use test-driven development
  • Use code reviews and pair programming
  • Treat documentation as code
  • Version control everything
  • Use continuous integration
  • Monitor everything critical

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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