Senior Data Engineer

Senior Data Engineer

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

Table of Contents

1. Trigger Phrases
2. Quick Start
3. Workflows
   • Building a Batch ETL Pipeline
   • Implementing Real-Time Streaming
   • Data Quality Framework Setup
4. Architecture Decision Framework
5. Tech Stack
6. Reference Documentation
7. 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

# 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

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

When to choose Lambda:

• Need to train ML models on historical data
• Complex batch transformations not feasible in streaming
• Existing batch infrastructure

When to choose Kappa:

• Event-sourced architecture
• All processing can be expressed as stream operations
• Starting fresh without legacy systems

Data Warehouse vs Data Lakehouse

Tech Stack

Reference Documentation

1. Data Pipeline Architecture

See references/data_pipeline_architecture.md for:

• Lambda vs Kappa architecture patterns
• Batch processing with Spark and Airflow
• Stream processing with Kafka and Flink
• Exactly-once semantics implementation
• Error handling and dead letter queues

2. Data Modeling Patterns

See references/data_modeling_patterns.md for:

• Dimensional modeling (Star/Snowflake)
• Slowly Changing Dimensions (SCD Types 1-6)
• Data Vault modeling
• dbt best practices
• Partitioning and clustering

3. DataOps Best Practices

See references/dataops_best_practices.md for:

• Data testing frameworks
• Data contracts and schema validation
• CI/CD for data pipelines
• Observability and lineage
• Incident response

Troubleshooting

→ See references/troubleshooting.md for details

When to Use

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.

Covers: Trigger Phrases, Core Tools, Generate pipeline orchestration config, Validate data quality, Optimize ETL performance.