Colin R. Moran

Good architecture starts with understanding constraints: technical, business, and organizational. Rather than chasing the latest trends, I focus on solutions that balance performance, maintainability, and team velocity. Incremental improvements over big-bang rewrites, always considering operational complexity.

When designing distributed systems, observability comes first. You can't fix what you can't see. I design for failure, plan for scale, and always consider the human operators who will maintain the system. Whether choosing between microservices and monoliths, or selecting a data processing framework, I evaluate tradeoffs explicitly and document the reasoning.

Production systems fail. The question is how quickly you can detect, diagnose, and resolve issues. I've seen too many systems where debugging production problems means grepping through logs or guessing at root causes. That's why metrics, tracing, and structured logging are integrated from the start.

The focus is on reducing mean-time-to-discovery (MTTD) and mean-time-to-resolution (MTTR). This means thoughtful alerting (not alert fatigue), comprehensive metrics (not just request counts), and clear runbooks. I've improved incident response by adding observability tooling, and I've seen how good monitoring can turn a 4-hour debugging session into a 10-minute fix.

On-call experience has taught me that reliability isn't just about preventing failures, it's about making failures manageable. Systems need graceful degradation, circuit breakers, and clear failure modes. When something breaks at 2 AM, the system should give you enough information to understand what's wrong and how to fix it.

After conducting over 100 engineering interviews, I've learned that hiring is about finding people who can grow, not just people who know specific technologies. The focus is on system design thinking, problem-solving approaches, and cultural fit. Candidates get clear feedback, and the process is treated as a two-way conversation.

Mentoring is about creating space for others to learn and grow. I've worked with junior and mid-level developers on everything from debugging production issues to designing their first microservices. In practice, this means pairing, code reviews as teaching moments, and helping people understand the "why" behind decisions, not just the "what".

Communities of practice are powerful ways to share knowledge and standardize approaches. I started a machine learning community of practice at CAS to help teams learn from each other and establish best practices. These communities work best when they're bottom-up, focused on real problems, and have clear goals. I've seen how they can transform organizational culture and accelerate learning.