desenyon/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

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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"

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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

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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:

• 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

| Feature | Warehouse (Snowflake/BigQuery) | Lakehouse (Delta/Iceberg) | |---------|-------------------------------|---------------------------| | Best for | BI, SQL analytics | ML, unstructured data | | Storage cost | Higher (proprietary format) | Lower (open formats) | | Flexibility | Schema-on-write | Schema-on-read | | Performance | Excellent for SQL | Good, improving | | Ecosystem | Mature BI tools | Growing ML tooling |

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Tech Stack

| Category | Technologies | |----------|--------------| | Languages | Python, SQL, Scala | | Orchestration | Airflow, Prefect, Dagster | | Transformation | dbt, Spark, Flink | | Streaming | Kafka, Kinesis, Pub/Sub | | Storage | S3, GCS, Delta Lake, Iceberg | | Warehouses | Snowflake, BigQuery, Redshift, Databricks | | Quality | Great Expectations, dbt tests, Monte Carlo | | Monitoring | Prometheus, Grafana, Datadog |

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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

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Troubleshooting

→ See references/troubleshooting.md for details