Matthew L. Swart

Solutions Engineering  ·  Cloud  ·  Technical Advisory

Matthew L. Swart

I help customers and technical teams turn ambiguous infrastructure constraints into decisions they can understand, trust, and act on.

Twenty years in and around production infrastructure  ·  Remote, United States

How I help

01

Discovery

Finding the constraint a customer actually has, which is usually not the one written in the requirements.

02

Tradeoff analysis

Laying out what each option costs later — in operational load, and in the freedom it removes from the next team.

03

Handoff design

Building the foundation so the next engineer extends it rather than routes around it.

Selected evidence

Cloud foundations

A reusable pattern the next engineer could extend

Senior cloud & DevOps engineer, enterprise software

Built a parameterized infrastructure-as-code pattern (AWS CloudFormation) that multiple customer environments were instantiated from — a foundation later engineers extended rather than replaced. Alongside it, a multi-account production AWS estate: networking, compute, managed databases, containers, IAM, designed with recovery in mind.

Range

Twenty years of walking into someone else's systems

Two decades of client-facing technical work, before moving in-house

Small businesses and professional offices, one unfamiliar environment after another — infrastructure, networks, web, support. Different industry every time, different constraints, same job: work out what actually matters here, then leave the owner able to run it. The customer-facing part was never a side of the work; it was the work.

Translation

A modernization case a business audience could weigh

Written for engineering and non-engineering decision-makers

Authored a business case that translated a technical modernization — trunk-based development and CI/CD — into a proposal non-engineers could evaluate and decide on. Earlier: UI development on regulatory compliance software, where the constraint was always what a non-specialist could be trusted to understand.

Current

Working fluently with AI systems, in practice rather than in theory

Ongoing, self-directed

I build and run my own AI tooling daily — agent workflows, retrieval, evaluation of whether the output is actually right. It has made me a careful reader of confident, well-formatted, wrong answers, which turns out to be the useful skill as these products reach customers.

Proof brief

Where this work goes well

Senior, customer-facing technical work, remote. Discovery, technical presentation, weighing tradeoffs, and helping someone reach a decision they can stand behind.

  • The customer conversation is part of the role, not an interruption to it
  • A support organization owns incidents, so escalation sits with the team built for it
  • The work is built for handoff — designed to outlast whoever built it
  • The product is technical enough that explaining it well actually changes the outcome

About

Most recently, three years as a senior cloud and DevOps engineer building and running production AWS environments for enterprise software. Before that, roughly two decades of client-facing technical work across a lot of different industries, and earlier still, UI development on regulatory compliance software.

The technology changed constantly. The job did not: work out what actually matters in a system somebody else built, and leave the people who own it able to run it.

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