╔══════════════════════════════════════════════════════════════════════════════╗
║                                                                              ║
║                ████████╗██╗███╗   ███╗ ██████╗ ████████╗██╗  ██╗██╗   ██╗    ║
║                ╚══██╔══╝██║████╗ ████║██╔═══██╗╚══██╔══╝██║  ██║╚██╗ ██╔╝    ║
║                   ██║   ██║██╔████╔██║██║   ██║   ██║   ███████║ ╚████╔╝     ║
║                   ██║   ██║██║╚██╔╝██║██║   ██║   ██║   ██╔══██║  ╚██╔╝      ║
║                   ██║   ██║██║ ╚═╝ ██║╚██████╔╝   ██║   ██║  ██║   ██║       ║
║                   ╚═╝   ╚═╝╚═╝     ╚═╝ ╚═════╝    ╚═╝   ╚═╝  ╚═╝   ╚═╝       ║
║                                                                              ║
║                           Timothy Nduati · @timothynn                        ║
║                                                                              ║
╚══════════════════════════════════════════════════════════════════════════════╝

From writing algorithms to architecting data flows. From debugging code to debugging insights.

I'm on a mission to transform how organizations leverage their data—combining software engineering rigor with data science intuition to build systems that don't just work, but scale.

Current Chapter: Transitioning from software engineering into data engineering, bringing battle-tested engineering practices to the data world.

skills = {
    'ml_frameworks': ['scikit-learn', 'tensorflow', 'pytorch'],
    'cloud_ml': ['Azure ML Studio', 'Azure Cognitive Services'],
    'analysis': ['pandas', 'numpy', 'scipy'],
    'visualization': ['matplotlib', 'seaborn', 'plotly'],
    'bi_tools': ['Power BI', 'Tableau']
}

toolkit = {
    'orchestration': ['Airflow', 'Azure Data Factory'],
    'processing': ['Spark', 'Kafka', 'dbt'],
    'storage': ['PostgreSQL', 'Azure SQL', 'Cosmos DB'],
    'cloud': ['Azure', 'AWS', 'GCP'],
    'streaming': ['Kafka', 'Azure Event Hubs']
}

foundation = {
    'languages': ['Python', 'Java', 'C++', 'JavaScript'],
    'paradigms': ['OOP', 'Functional', 'Concurrent'],
    'practices': ['TDD', 'CI/CD', 'Code Review'],
    'tools': ['Git', 'Docker', 'Kubernetes']
}

automation = {
    'containers': ['Docker', 'Kubernetes'],
    'cicd': ['GitHub Actions', 'Azure DevOps'],
    'monitoring': ['Prometheus', 'Grafana'],
    'iac': ['Terraform', 'ARM Templates']
}

Code is temporary. Data systems are forever.

Good data engineering isn't just about moving data from A to B—it's about building resilient, self-healing systems that handle chaos gracefully. It's software engineering principles applied to the messiest resource we have: real-world data.

My Principles:

• 🎯 Quality over speed → But never sacrifice both
• 🔄 Automate everything → Including the automation
• 📊 Data tells stories → Make sure they're true
• 🧪 Test in production → Just kidding. Test before production.
• 🚀 Ship iteratively → Perfect is the enemy of shipped

• 🏗️ Building a real-time data pipeline for streaming analytics
• 📚 Deep diving into distributed systems and data modeling
• 🤖 Implementing MLOps practices for production ML systems
• 🌱 Contributing to open-source data tools
• 📝 Writing about lessons learned in data engineering

I'm always interested in discussing data architecture, ML systems, or how to make data pipelines that don't keep you up at night.

graph LR
    A[Software Engineering] --> B[Data Science]
    B --> C[Machine Learning]
    C --> D[Data Engineering]
    D --> E[Distributed Systems]
    
    style A fill:#0D1117,stroke:#58A6FF,stroke-width:2px
    style B fill:#0D1117,stroke:#58A6FF,stroke-width:2px
    style C fill:#0D1117,stroke:#58A6FF,stroke-width:3px
    style D fill:#0D1117,stroke:#F97316,stroke-width:3px
    style E fill:#0D1117,stroke:#F97316,stroke-width:2px