"If it does not compute, you must reboot."

Senior software engineer in Buffalo, NY, with 10+ years spent on the backend of things people actually depend on — commercial lending platforms, automotive CRM, e-commerce review infrastructure. Java and Spring Boot are home base. AWS is where most of it runs.

I'm a self-starter and a stubbornly lifelong learner, which is a polite way of saying I have never once left a "how does that work?" alone.

I'm deliberately steering toward data engineering — streaming systems, pipelines, ETL — in Java and Python. It's less a pivot than a return: some of my favorite work has always been the plumbing that moves data at scale reliably, and I'd rather build that on purpose than in passing.

Open to senior backend and data engineering roles, plus scoped contract work.

• Kafka → AWS MSK migration under a hard contract deadline, replacing a Confluent agreement before it rolled to month-to-month pricing. Landed on time, saved real money annually.
• Query and pipeline work against Vertica on datasets in the billions of records — the kind of scale where a careless join is a career event.
• Angular → React framework migration, rebuilt from the ground up rather than ported-and-hoped-for-the-best.
• Dependency vulnerability triage as a standing practice — nightly CVE scans across a fleet of Java applications, remediated inside normal release cycles instead of as emergencies.
• Release coordination across seven development teams, running a two-sprint monthly deployment cadence through sandbox promotion, sign-off, and handoff.
• Azure landing zone foundations for a planned Spring Boot–to–serverless rewrite, aligning requirements across multiple internal cybersecurity teams with competing standards.

• #100DaysOfCode, journaled publicly on my blog — the accountability is the point.
• Quantum computing, via Qiskit Global Summer School. No, it isn't immediately practical. Yes, I'm doing it anyway.
• Local LLMs and agent tooling — running models on my own hardware, retrieval over my own documents, learning where the abstractions leak.

I knit and crochet, which turns out to share more with software than anyone expects: both are deterministic systems built from a single continuous thread, both fail loudly three rows after the actual mistake, and both have a robust culture of reading other people's patterns before writing your own.

I'm also interested in teaching technology to people the industry forgets — particularly older adults who'd like to keep up with their grandkids and haven't been given a patient on-ramp.

_{Open to conversations about data engineering roles, contract work, or why your yarn stash is technically a database with no indexes.}