🚀 Data Engineering Portfolio

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📌 Overview

Welcome to my Data Engineering Portfolio.

This repository showcases real-world, job-ready data engineering projects designed to simulate how modern data platforms are built and maintained in production environments.

Rather than isolated scripts or notebooks, these projects emphasize: - Pipeline structure\

• Data modeling logic\
• Validation and testing\
• Reproducibility\
• Engineering best practices

This portfolio is intentionally designed to reflect how data engineering work looks in real companies.

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🧠 Skills Demonstrated

• Python for data pipelines and automation\
• SQL for analytics engineering\
• dbt (models, tests, analytics engineering patterns)\
• Data modeling (staging, marts, star schemas)\
• Incremental loading & SCD2 strategies\
• Data quality validation (schema, nulls, uniqueness, freshness)\
• Modular project structuring\
• Reproducible environments (requirements.txt)\
• Version control workflows (Git/GitHub)

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📂 Repository Structure

data-engineering-portfolio/
│
├── projects/              # Portfolio projects
│   ├── project_01/
│   ├── project_02/
│   └── ...
│
├── assets/
│   └── diagrams/
│       └── hero-architecture.png
│
├── shared/                # Shared utilities/helpers
├── run_demo_all.sh        # Run pipelines locally
├── requirements.txt       # Python dependencies
└── README.md              # You are here

Each project typically includes: - A project-specific README\

• Clear problem statement\
• Pipeline logic\
• Sample datasets\
• Outputs\
• Validation logic\
• Real-world framing

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⚙️ Quickstart (Run Locally)

You can run the portfolio locally:

# Clone repository
git clone https://github.com/JamieChristian22/data-engineering-portfolio.git
cd data-engineering-portfolio

# Create environment
python -m venv venv
source venv/bin/activate  # Mac/Linux
venv\Scripts\activate     # Windows

# Install dependencies
pip install -r requirements.txt

# Run demo pipelines
bash run_demo_all.sh

This simulates: - Ingestion\

• Transformations\
• Data modeling\
• Validation\
• Analytics outputs

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📊 Concepts Covered Across Projects

Projects across this portfolio demonstrate experience with:

• Batch data pipelines\
• Streaming & micro-batch simulation\
• Change Data Capture (CDC-style logic)\
• Raw → Staging → Analytics layers\
• Incremental models\
• Slowly Changing Dimensions (SCD2)\
• Business-ready data marts\
• Data quality testing\
• Reproducible workflows

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🎯 Purpose of This Portfolio

Many portfolios only show dashboards or notebooks.

This portfolio focuses on: > How data moves, transforms, scales, breaks, and gets validated in real systems.

It is designed to support roles such as: - Data Engineer\

• Analytics Engineer\
• Analytics-focused Data Analyst\
• BI Engineer\
• Modern analytics stack roles

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👤 About Me

Jamie Christian
Data-focused professional building realistic, job-ready portfolios across:

• Data Engineering\
• Data Analytics\
• Financial Analytics\
• Product Analytics\
• Cloud Architecture

🔗 LinkedIn: https://www.linkedin.com/in/jamiechristian2\ 🔗 GitHub: https://github.com/JamieChristian22

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⭐ For Recruiters & Hiring Managers

If you're reviewing this repository:

• Browse the /projects folder\
• Review the code and structure\
• Explore project READMEs\
• Feel free to connect via LinkedIn

This portfolio is actively maintained and expanded.