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BenWam254/README.md

Benny Wambua

Data Engineer & Systems Developer | Web Infrastructure & Analytics

I engineer high-performance web applications and automated data pipelines at Forge Digital Solutions, focusing on precision execution. Clean UI architectures, resilient backends, and zero-maintenance automation workflows. My development philosophy centers on maintainable code, high-contrast dark-mode design systems, and robust data transformations that perform reliably at scale.

Stack & Ecosystem

Core Technologies Python, JavaScript (ES6+), TypeScript, SQL, HTML5, CSS3/Tailwind

Engine & Analytics Pandas, Polars, DuckDB, PostgreSQL, Matplotlib, Seaborn

DevOps & Workflow Docker, WSL (Ubuntu), Git, Vercel, Jupyter, Obsidian

Current Focus

Expanding deep expertise in Data Engineering and analytical pipelines—specifically containerized Docker environments, local DuckDB/Polars processing, and structured ETL workflows. Engineering systems that seamlessly ingest, transform, and visualize complex datasets with minimal latency.

Pinned Loading

  1. infrastructure-intelligence-dashboard infrastructure-intelligence-dashboard Public

    A high-performance, production-grade Next.js decision interface and infrastructure intelligence dashboard built with TypeScript. Features a minimalist, high-contrast visual identity optimized for r…

    TypeScript

  2. benny-wambua-portfolio benny-wambua-portfolio Public

    Personal developer portfolio engineered for clarity, scannability, and recruiter-ready technical documentation.

    HTML 1

  3. global-energy-transition-eda global-energy-transition-eda Public

    Production-grade exploratory data analysis examining global renewable energy adoption, capacity trends, and emission dynamics using Python, Pandas, and publication-ready Matplotlib visual pipelines.

    Jupyter Notebook

  4. moma-cultural-heritage-eda moma-cultural-heritage-eda Public

    Exploratory data analysis and visual study analyzing acquisition velocity, curatorial shifts, and historical collection scale across 150,000+ catalogued works in the MoMA collection using Python an…

    Jupyter Notebook