Senior-level data engineering expertise for building scalable pipelines, ETL systems, and production data infrastructure.
Works with
Covers advanced patterns across data pipeline architecture, modeling, and DataOps with distributed computing frameworks (Spark, Airflow, dbt, Kafka) and modern data stack tools (Databricks, BigQuery, Snowflake)
Includes production deployment patterns for scalable data processing, ML model serving with low latency, and real-time inference with auto-scaling and monitor
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionsenior-data-engineerExecute the skills CLI command in your project's root directory to begin installation:
Fetches senior-data-engineer from davila7/claude-code-templates and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate senior-data-engineer. Access via /senior-data-engineer in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
9
total installs
9
this week
24.2K
GitHub stars
0
upvotes
Run in your terminal
9
installs
9
this week
24.2K
stars
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
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
davila7/claude-code-templates
alirezarezvani/claude-skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
We added senior-data-engineer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: senior-data-engineer is focused, and the summary matches what you get after install.
Solid pick for teams standardizing on skills: senior-data-engineer is focused, and the summary matches what you get after install.
We added senior-data-engineer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
senior-data-engineer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
senior-data-engineer has been reliable in day-to-day use. Documentation quality is above average for community skills.
senior-data-engineer reduced setup friction for our internal harness; good balance of opinion and flexibility.
senior-data-engineer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: senior-data-engineer is the kind of skill you can hand to a new teammate without a long onboarding doc.
senior-data-engineer has been reliable in day-to-day use. Documentation quality is above average for community skills.
showing 1-10 of 38