MarketAxess Innovation & Technology Culture

How does your team stay ahead of emerging technology trends while scaling fast?

We stay ahead by making experimentation part of our normal engineering cadence, not something we squeeze in on the side. We experiment freely with frontier tools and frameworks, which keeps our instincts sharp. But we go further than that: We deliberately allocate 10 to 20 percent of our capacity to fold promising discoveries into our production systems and pay down technical debt. We keep a tight feedback loop through production telemetry, post-incident learnings and developer experience metrics so we’re only adopting new tech once it can measurably improve reliability, velocity or cost.

Software engineering, like other types of engineering, relies on having sound, time-tested processes alongside choosing the right tool for the right job. You can’t know you have the right tool unless you’re continually evaluating what’s out there — and there’s no way to realistically do that unless you make the time as part of your routine work schedule.

What recent product or feature are you most proud of — and what impact has it had?

I’m most proud of the “living architecture” capability we’ve built. We use an architecture-as-code approach to generate high-fidelity technical documentation that stays constantly in sync with our codebase. The result is a real-time architecture diagram for our system, enriched with health signals from multiple data sources — observability metrics, alerting and deployment status — all in one view.

On top of that, we’re building a custom server that lets AI agents query system health directly. Anyone on the team can now debug and trace issues conversationally, without needing deep knowledge of every service and how it relates to the system as a whole. What used to take an experienced engineer precious minutes of log-diving can now be surfaced in seconds. That’s been powerful in how quickly we can analyze and then respond to production issues. More broadly, we’ve been intentional about using AI advances to improve how we actually work — not as a novelty, but as infrastructure that compounds over time.

How do you create a culture where innovation and experimentation are encouraged daily?

We deliberately carve out dedicated innovation periods where the whole team sets aside feature work and brainstorms ideas that could improve our platform or how we work. Any exciting moonshot idea gets a time-boxed spike to see if it holds up, and it’s inspiring how often it does.

We’ve already shipped features, internal tools and process improvements directly out of these sessions. The team runs casual “show and tell” demos afterward, and the energy is infectious; there’s a genuine excitement about what’s possible and where development is headed.

The key is making experimentation a scheduled, sanctioned part of how we work, not something people have to sneak in around the edges. When your team knows that exploration is valued, not just tolerated, they show up with better ideas and more willingness to take smart risks.

What tools support your day-to-day work?

My toolkit reflects the balance of engineering management: staying close to the technical work while keeping teams unblocked. On the technical side, I rely heavily on Kubernetes, Kafka and AWS, which power our event streaming and orchestration infrastructure. FreeLens has become a daily driver for managing our Kubernetes clusters, and I spend a lot of time in Git. I’ve also been leaning more on AI-assisted development through GitHub Copilot, which is increasingly useful for code review, exploration and getting unstuck on unfamiliar parts of the codebase.

On the management side, it’s the usual suspects: Microsoft Teams for communication, Jira for tracking, and well-structured one-on-ones. I also use local LLMs to think through problems, draft communications, and explore ideas before bringing them to the team. I have found that having a tool that lets you reason out loud without interrupting anyone is highly valuable.

How does your team experiment?

We treat experimentation as a normal part of engineering, not a separate activity. The teams I lead operate distributed systems where assumptions break in surprising ways, so we build in space to test ideas before committing to them. That includes spike branches, proof-of-concept environments and a culture 

We also encourage exploration around emerging tooling. AI tooling is the obvious example right now. Rather than mandating one approach, our engineers try different workflows and share what works. Some of the best ideas tend to come from quick experiments that surface in team syncs. I stay hands-on with these experiments myself, both because I want a real point of view on what’s working and because the best way to know whether a tool belongs in our stack is to use it on actual problems.

How does MarketAxess adapt to change? 

The teams I lead are product teams, which means we feel priority shifts directly. New initiatives land, timelines compress, and we need to deliver without compromising quality or reliability.

One pattern that’s worked well is reusing proven implementations rather than rebuilding from scratch. When there is a new product request, our first question is whether we already have a pattern, service or component that solves most of the problem. More often than people expect, the answer is “yes.” This mindset has helped us shorten time to market on several initiatives and free up engineering capacity for the truly novel work that requires custom solutions.

It sounds simple, but it requires real discipline. It means investing in shared infrastructure and resisting the engineer’s natural pull toward greenfield work. When it clicks, it’s one of the highest leverage things a platform-oriented organization can do.