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data-engineering-data-pipeline

You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.

Install

mkdir -p .claude/skills/data-engineering-data-pipeline-michael-bodo && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/13763" && unzip -o skill.zip -d .claude/skills/data-engineering-data-pipeline-michael-bodo && rm skill.zip

Installs to .claude/skills/data-engineering-data-pipeline-michael-bodo

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.
154 charsno explicit “when” trigger

About this skill

Data Pipeline Architecture

You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.

Use this skill when

  • Working on data pipeline architecture tasks or workflows
  • Needing guidance, best practices, or checklists for data pipeline architecture

Do not use this skill when

  • The task is unrelated to data pipeline architecture
  • You need a different domain or tool outside this scope

Requirements

$ARGUMENTS

Core Capabilities

  • Design ETL/ELT, Lambda, Kappa, and Lakehouse architectures
  • Implement batch and streaming data ingestion
  • Build workflow orchestration with Airflow/Prefect
  • Transform data using dbt and Spark
  • Manage Delta Lake/Iceberg storage with ACID transactions
  • Implement data quality frameworks (Great Expectations, dbt tests)
  • Monitor pipelines with CloudWatch/Prometheus/Grafana
  • Optimize costs through partitioning, lifecycle policies, and compute optimization

Instructions

🧠 Knowledge Modules (Fractal Skills)

1. 1. Architecture Design

2. 2. Ingestion Implementation

3. 3. Orchestration

4. 4. Transformation with dbt

5. 5. Data Quality Framework

6. 6. Storage Strategy

7. 7. Monitoring & Cost Optimization

8. 1. Architecture Documentation

9. 2. Implementation Code

10. 3. Configuration Files

11. 4. Monitoring & Observability

12. 5. Operations Guide

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