Practical engineering for ambiguous systems work

I’m a senior software engineer who is strongest where requirements are unclear, systems cross team boundaries, and the work needs both implementation and judgment. I build and support cloud-backed applications, data workflows, full-stack tools, and automation across Python, TypeScript, C#, SQL, AWS, and GCP.

In enterprise platform work, I’ve built three Python applications, automated about 100 scheduled reports, supported tens of thousands of account records across five sites, and contributed to warehouse workflows spanning hundreds of tables and millions of daily-consumed records. Previously, I supported data and reporting systems used by 12 organizations and roughly 200 users or departments, added about two hours of daily application availability, and helped a two-to-three-person shared-ownership team reduce recurring incidents from roughly 10–20 per week to nearly zero non-requirement-related issues over about one year.

Before software, I served in the U.S. Air Force maintaining radar systems and recruiting across four counties. That background shaped how I troubleshoot, communicate, train others, and take responsibility for operational outcomes. I use AI in the same practical way: as leverage for planning, implementation, review, and documentation, with human approval and technical ownership kept in the loop.