DevOps engineer & data enthusiast with a background in mathematics, machine learning, and creative visualization.
I build projects that connect data workflows, visualization, and cloud infrastructure. Recently, I’ve focused on automation that makes systems easier to manage, more reliable, and harder to break.
| Project | Focus Area | Workflow Signals | Product Output |
|---|---|---|---|
| World Life Expectancy | SQL & Tableau Data Analysis | README Walkthrough | SQL and CSV files, Tableau Dashboard |
| Customer Churn Prediction | Full data pipeline & workflow | .github/ templates, issues, milestones, branching history |
SQL cleaning → Python modeling → Tableau dashboard |
| LEGO Dataset Dashboard | Visualization & product delivery | Lightweight Git, minimal issue/PR workflow | PHP web interface → Tableau dashboards |
| AWS Cost Analyzer | Cloud cost analysis (FinOps) | Python scripts with clean repo structure | Synthetic AWS cost dataset → Tableau dashboards |
| Visualizations | Generative & artistic analysis | Lightweight repo structure | Processing.org sketches + Python data science for creative visuals |
This project is a learning project from AnalystBuilder.com. We performed data cleaning on a set with SQL, did some exploratory data analysis, and I extended the project to include a Tableau dashboard.
This is a learning project, I used AI assisted templates for code to review MLOps Concepts. I performed the planning of the project as if I were a project manager creating milestones, issues, PRs, actions, labels, code reviews, etc. It is an ongoing and I plan to expand it as I learn. It is a churn prediction pipeline starting with SQL cleaning scripts, moving into Python feature engineering and modeling, and finishing with a Tableau dashboard for results visualization. The repo is structured like a real-world product build — with .github/ templates, issues, milestones, and branching history — to highlight not just the analysis but also the Git workflow and project management practices behind it.
This project focuses on Web Development with PHP, CSS, JS.
Using the Kaggle LEGO dataset, I designed Tableau dashboards to explore theme popularity, set complexity, and color usage across LEGO history. A lightweight PHP web interface supports the data, and the project could be containerized with Docker for portability.
Unlike the churn repo, this one emphasizes product delivery (interactive dashboards) rather than a workflow-heavy pipeline — providing a complementary showcase of my skills.
This project highlights my FinOps and cloud cost analysis skills.
I used AI to generate a synthetic AWS billing dataset (services, accounts, environments, teams). The results were visualized in Tableau dashboards to surface KPIs like MTD spend, service-level costs, and team allocations.
The project demonstrates my ability to connect cloud infrastructure knowledge with data analysis to support cost optimization — a bridge between my DevOps background and data-focused career path.
This repo highlights my work in data and signal visualizations using both Python and Processing.org.
It includes experiments like K-Nearest Neighbor variations in Python as well as a wide range of generative animations in Processing — from sinusoidal waves, knot curves, and polygon spirals to gravity simulations, graph algorithms, and artistic distortions.
The collection blends data science, algorithmic thinking, and creative coding, showcasing how technical models can be transformed into engaging visual experiences. Click here or image to watch the video
https://github.com/jbrdge/MySQL-for-Data-Analytics, fork of https://github.com/AnalystBuilder/MySQL-for-Data-Analytics.
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BA in Comprehensive Mathematics – University of Arizona
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MS in Electrical & Computer Engineering – University of Arizona