Andrew Dennis

Andrew Dennis plays a central role in advancing the data capabilities that support investment decision-making at Aperture Investors. As Director of Data and Analytics, he leads the development of technology, applications and intelligent workflows that help both front-office investment professionals and business teams make better use of information while streamlining workflows across the firm. Bringing together data expertise, software development and business insight, Dennis has helped establish the digital foundation that enables information to move efficiently across the organization while creating lasting business value.

The Foundation Behind Smarter Innovation

Throughout my career, one lesson has remained constant. Before you focus on innovation, you have to build a foundation that allows it to last.

When I joined Aperture Investors a little over three years ago, we were just beginning that journey. We had talented investment professionals and clear ambitions for growth. What we didn't yet have was the data infrastructure to support those ambitions at scale.

That made our priority clear. We focused first on building the systems that would support everything that followed, not chasing the latest technologies.

New technology creates opportunity, but progress still depends on getting the fundamentals right.

The Beginning of Something Bigger

The first project that truly changed our trajectory came from a practical business need.

Our risk team’s reporting still relied on certain manual processes. Producing portfolio risk reports could therefore take longer than it needed to.

At that point, I was the only developer working on the initiative. I built an automated workflow that extracted portfolio data, integrated it with our external risk analytics provider and stored the results in what became our first centralized database.

It became much more than an automation project.

Creating that system required establishing the firm’s security master and organizing information in a way that could support future development. Once that foundation existed, it triggered a wave of new requests because teams could see more clearly what was possible.

Risk reporting became timely and consistent. More importantly, data began moving across the firm in a more structured way.

  New technology creates opportunity, but progress still depends on getting the fundamentals right.  

Since then, we have continued expanding that foundation toward a fully integrated data environment where reporting, analytics and internal applications work together. The journey continues, but those early projects created the momentum that made everything else possible.

Choosing Progress with Purpose

The pace of change today is unlike anything I have experienced before.

Every week introduces another technology or AI breakthrough. That creates pressure to adopt everything at once.

I have found that restraint is just as valuable as curiosity. We avoid adopting technology simply because it is generating attention.

Our team generally evaluates new technologies through proof-of-concept work before deciding where they add value. At the same time, we continue relying on technologies that have proven themselves over time. Conversations with trusted peers across the industry also help validate what we are seeing.

Sometimes a newer approach genuinely improves how we work. Sometimes an established solution remains the better answer.

The responsibility is not choosing between innovation and reliability. It is understanding where each belongs.

Our data infrastructure gives us the flexibility to adapt without the constraints of decades-old legacy systems. Every decision still comes back to one principle. Technology should solve real problems before introducing new complexity.

AI as a Force Multiplier

A few years ago, building sophisticated internal applications required significant development time and specialized front-end expertise. For a lean technology team, many worthwhile ideas never moved beyond the drawing board because the investment was simply too high.

That equation has changed. AI has changed the economics of software development for smaller teams.

We can now build faster, test ideas that once felt out of reach and deliver solutions with fewer resources. AI extends our capabilities rather than replacing them.

Its greatest value is speed. It accelerates development and allows us to focus more on refinement instead of building everything from scratch.

That opportunity also brings responsibility.

Every new capability raises questions around security, governance and cost. Those questions matter as much as the technology itself because speed only creates value when it is controlled.

Making AI Work Responsibly

One area that deserves attention is discipline.

AI makes it easy to generate code, analyze information and build applications. It also makes it easy to consume unnecessary resources or expose data if not properly managed.

That is why governance receives as much attention as capability.

Our approach is to build controlled interfaces that give teams efficient access while protecting the integrity of core systems. We also focus on usage efficiency. Large language models become expensive when workflows are inefficient, so we create reusable tools that eliminate the need to solve the same problems from scratch each time.

Technology should reduce complexity, not create new layers of it.

The goal is not to deploy AI everywhere. It is to use it where it meaningfully improves the work while maintaining the standards expected within an investment firm.

Where Curiosity Becomes Capability

Building technology is only part of the job. Building the right team matters more.

I have been fortunate to work alongside naturally curious people. That matters more than mastering any single programming language because technology evolves too quickly for fixed expertise to remain relevant for long.

My own career has moved across data science, software engineering and data engineering. I still stay closely involved in the work, often working alongside my team on projects instead of stepping away from the technical side. That helps me understand challenges directly and keep ideas moving.

Creativity also needs room.

Some of our best solutions began as uncertain ideas. I encourage people to test those ideas, even when the outcome isn't guaranteed.

If every project follows the safest path, experimentation disappears. The strongest developers I have worked with are willing to test assumptions and learn from outcomes, even when success is not guaranteed.

AI has shortened the distance between an idea and a prototype. Teams can experiment faster, learn faster and improve faster.

Creating that environment matters more than enforcing a single way of working.

The Mindset That Sustains Growth

One lesson I would share is to say yes to challenges that stretch your capabilities.

Earlier in my career, the projects that felt too ambitious or outside my experience were exactly how we learned — and plenty of them took much longer than planned, if they came together at all. The struggle was the lesson, even when the product never shipped. Today's tools change that. We can learn while building, take on unfamiliar technologies, and actually solve problems that once felt unrealistic. That does not remove the need for careful thinking. It simply expands what becomes possible.

Some of our most rewarding work began because we were willing to say yes before we had every answer. Each project built knowledge that made the next one easier.

Technology will continue to evolve, and today's breakthroughs will become tomorrow's standard practice.

What endures is curiosity.

If you remain open to learning, willing to experiment and grounded in strong fundamentals, you will continue growing regardless of how quickly technology changes. That mindset will continue to distinguish effective data leaders long after today’s tools have evolved into something entirely different.