CST 349 is about connecting your major to actual career goals. Students analyze information from academic and industry sources and talk directly with working professionals. The focus is on problem-solving, communication, and teamwork, along with developing a professional value proposition and starting to build a portfolio.
For this Industry Expert Interview, I spoke with Matt Valancy, a CSUMB CS Online alumnus with a career largely centered on test engineering and applied technical work. I selected Matt because we previously connected when I was exploring the CSUMB CS Online program, and he had firsthand experience completing the program and working in the field. I wanted a realistic view of what day-to-day work can look like, what skills truly matter early, and how industry expectations are shifting.
A central theme of our conversation was how AI has changed the industry's "difficulty curve." Matt described AI as a major productivity amplifier that reduces the day-to-day burden of manual coding and repetitive problem solving. However, he emphasized that this does not remove the need for strong fundamentals. Instead, the challenge shifts toward higher-level decision-making: choosing what to build, ensuring it is reliable, and making something that people genuinely want to use.
Matt also described his own path from CSUMB into industry. His early experience involved embedded work and hands-on exposure through an environment connected to underwater robotics. Over time, this evolved into a long-term focus as a test engineer, where reliability, validation, and measurement are constant priorities.
On the practical side, Matt's advice was strongly oriented around building demonstrable, real-world skills. He encouraged publishing work publicly rather than keeping projects as local scripts, and suggested a clear entry point: hosting a simple static portfolio or project site using GitHub and Cloudflare Pages. He also emphasized the value of monitoring and observability, recommending a time-series stack using Telegraf, InfluxDB, and Grafana. In his view, understanding how to measure systems and interpret their behavior is a valuable skill that translates across roles and industries.
Finally, Matt repeatedly referenced the importance of "shapes," which I interpreted as the importance of structuring information and interfaces so tools can work together reliably. In practice, this means defining consistent formats for metrics, logs, and system signals so they can be routed cleanly into databases, dashboards, and potentially AI-based analysis.
This interview helped me understand that career readiness is not only about "learning to code," but about learning to build systems that are visible, measurable, and useful. I also came away with a clearer sense of how AI changes the expectations placed on engineers. If AI increases output, then the differentiators become system thinking, product judgment, and the ability to validate and monitor what you build.
The conversation also broadened my view of career options. I previously thought mostly in terms of general software engineering, but Matt's perspective highlighted how reliability-minded roles like test engineering still demand strong technical ability while emphasizing measurement and verification. That aligns well with a practical mindset: if you can build, monitor, and explain systems clearly, you can contribute in many roles.
I plan to apply these insights by shifting some of my time toward building a portfolio that is both public and practical. My first step will be to push a small project to GitHub and deploy a simple static site using Cloudflare Pages so my work is accessible and easy to review. Next, I want to build foundational monitoring skills by setting up Telegraf, InfluxDB, and Grafana in a controlled environment and creating dashboards for CPU, memory, disk, and network metrics. If I progress quickly, I will add a small analysis layer that summarizes changes or flags anomalies, using structured data formats to keep the system understandable.
Lastly, I will continue balancing coursework and interview preparation with more intentional planning. Overall, Matt's advice reinforced that practical projects and real system skills can support both academic success and long-term career readiness.