senior-data-engineer

Files (10)

• LICENSE
• SKILL.md
• data_modeling_patterns.md · references/
• data_pipeline_architecture.md · references/
• dataops_best_practices.md · references/
• troubleshooting.md · references/
• workflows.md · references/
• data_quality_validator.py · scripts/
• etl_performance_optimizer.py · scripts/
• pipeline_orchestrator.py · scripts/

SKILL.md (first lines)

# Senior Data Engineer

Production-grade data engineering skill for building scalable, reliable data systems.

## Table of Contents

1. [Trigger Phrases](#trigger-phrases)
2. [Quick Start](#quick-start)
3. [Workflows](#workflows)
4. [Architecture Decision Framework](#architecture-decision-framework)
5. [Tech Stack](#tech-stack)
6. [Reference Documentation](#reference-documentation)
7. [Troubleshooting](#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

```bash
# 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:**

From Kilo-Org/kilo-marketplace, Apache-2.0.

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