Backend thinking, creative output.
I came to software through a winding but useful path: music, technical support, SQL, ETL,
Python, data infrastructure, and now AI-assisted software development. That background
shaped how I build: what is the data, what are the relationships, what needs to move, what
needs to be tracked, and what needs to be trusted?
From there, I like turning a sturdy foundation into practical tools — pipelines, reporting
layers, internal apps, automations, web interfaces, and project-specific utilities that make
recurring work easier to understand.
How I got here
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My path started in technical and technical-support roles where I learned SQL,
operations, troubleshooting, and how business systems behave in the real world.
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From there, I moved into SSIS-style ETL work, proprietary Python ETL applications,
metadata infrastructure, file execution and tracking systems, scheduling systems,
reporting layers, partner data exchanges, API integrations, and internal data lakes.
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More recently, I’ve moved closer to full-stack software development. AI tools help
me move faster through interfaces and unfamiliar patterns, while the data and backend
foundation keeps the work grounded.
What I like building
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Software that sits close to real operational problems: ETL pipelines, file and API
exchanges, metadata-driven workflows, internal tools, reporting layers, scheduling
systems, data lakes, lightweight web applications, and project-specific utilities.
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Work where practical engineering and creative exploration meet.
Data modeling
Python ETL
Metadata systems
File tracking
Scheduling tools
Reporting layers
Data lakes
Backend logic
Lightweight web apps
Unit Testing
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Unit tests and integration tests are not just safety nets. They help define behavior,
clarify boundaries, catch regressions, and make refactoring less scary.
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Fast prototyping makes tests even more important because they prove that quick
changes are still reliable changes.
