Technology Blog

We sat down with Pat Cooney, Head of Platform Engineering, to talk about what Platform Engineering means here and where agentic AI fits into the picture. Pat has spent over a decade at Optiver across markets, regions, and roles.

When people talk about developer productivity, they often jump straight to tools: powerful coding agents, faster compilers, smarter automation. These things matter, but they are not the whole story.

In most systems, the database acts as a boundary. You write data into it, and other systems read from it. If you need something more dynamic, like reacting to changes as they happen, you usually introduce something alongside it, whether that is a service layer, a queue, or a stream.

Large language models (LLMs) are getting surprisingly good at learning the basics of trading. Consider that the latest models are able to perform tasks like pricing simple scenarios, reasoning through rules and even outlining basic strategies.

If a UI doesn’t feel instant, it feels broken and users start to question what they’re seeing. A grid lags, values don’t update when expected, or a filter that used to feel instant starts to slow down. In high-demand systems like a trader’s workstation, a single desktop may be running many latency-sensitive applications at once, all competing for CPU, memory, and network bandwidth, so issues can show up quickly.

Data systems are often described along two axes: speed and scale. In practice, “speed” usually means some combination of latency and throughput, and systems are often optimized for one at the expense of the other, sometimes by trading efficiency for raw capacity. Those distinctions tend to break down quickly once systems move beyond simple use cases. Once a system is both data-heavy and interactive, speed and scale stop being independent variables. Decisions made to improve one almost always affect the other, sometimes in ways that are not immediately obvious and only surface under real usage.

In trading, speed and insight go hand in hand. At the 2025 Databricks Data + AI Summit, Optiver shared how we built low-latency, self-serve dashboards by integrating Databricks Apps with Dash. The result: real-time market visibility that scales with both data and decision-making.

At this year’s EuroPython, Optiver Senior Software Engineer and Team Lead Samet Yaslan delivered a timely talk for developers working on performance-critical systems: “Choosing between free threading and async.”

Meet Cian Lane, Optiver’s Global Head of Developer Experience. Over the course of five years as a Software Engineer with us, Cian has worked across multiple teams in both our Amsterdam and London offices. Through this cross-regional experience, he’s been able to develop a strong sense of what helps developers to be productive at Optiver, and what slows them down.

Meet Kevin Sprague, Hardware Engineering Lead at Optiver. He joined as an FPGA Engineer in 2015 and now plays a key role in designing bespoke hardware solutions built to perform under the demands of modern markets.

Achieving ultra-low latency in trading systems starts at the design level. At CppCon, David Gross, Options Tech Lead, delivered a must-watch talk on building high-performance trading systems using C++.

Curious about the technology that powers successful trading? At Optiver, our engineers are the backbone of our operations, driving innovations in pricing, risk management, and execution. From designing high-speed systems for real-time trade execution to developing sophisticated models for accurate pricing and advanced risk management, our team ensures optimal performance in a fast-paced trading environment.

In this post, I’ll lay out some of the challenges, framed in the context of IaC, and how we solved them. Many of our solutions are bespoke implementations of popular concepts, an advantageous approach that allows us great flexibility of implementation. We’ll cover two areas: device data audits, also known as our secret grey field IaC recipe, and device provisioning.

In the high-performance landscape of algorithmic trading, technological infrastructure isn’t just important—it’s critical. While Infrastructure as Code (IaC) is a well-established practice in cloud-based solutions, its application in non-cloud environments presents unique challenges, especially in latency-sensitive environments like ours at Optiver. In this post, I’ll go into these specific challenges and the solutions we’ve developed at Optiver.

When we think of market making, we often associate it with low-latency C++ applications. However, many other technologies and programming languages play a crucial role in establishing Optiver as a leading global trading firm, among them being C#.

Data visualisation is at the core of effective decision-making in the fast-paced, data-driven world of trading. For Optiver’s traders to access the information they need right when it matters, it’s crucial to display data intuitively and meaningfully. In this blog post, we’ll take you behind the scenes of data visualisation at Optiver and explore how our engineers collaborate with traders to optimise workflows, manage compute power and tackle the challenges of latency and volume through innovative technological solutions.

Working at a trading firm like Optiver provides unique opportunities for individuals to grow and enhance their careers – especially as an engineer. Not only does the culture encourage bold idea sharing and concept creation, but the work environment itself presents different challenges and opportunities that simply aren’t as prevalent in other industries. All of these things combined pave the way for continuous career development and rapid improvement.