Why I Joined Eventual

Sam Stokes, Software Engineer at Eventual, shares why he joined the team and what excites him about the work ahead.

I am joining Eventual, the company behind Daft, as a software engineer to help shape the long-term architecture of our cloud platform.

How I Found My Way Into Distributed Systems

I became a distributed systems engineer by accident. In 2010, I was working on a small startup that suddenly picked up tens of thousands of users overnight. This was great for fundraising and terrible for availability. I had to learn about horizontal scaling, rate limits, queues, backpressure, and database replicas on the fly. It was a chaotic stretch, full of late nights and services catching fire, but I was hooked. I realized that I enjoyed the puzzles that distributed systems bring.

A few years later, I joined Honeycomb. Their customers needed to store and analyze large volumes of observability data, and Honeycomb had built a specialized, distributed store from scratch to support it. It was a bold choice for a young company, but it gave customers both speed and flexibility. Seeing how much better the product was because of that foundation was energizing, and presenting the system at Strange Loop remains a highlight of my career.

What Makes Distributed Systems Hard for Most Teams

Even though I love this work, I know distributed systems is not everyone’s favorite topic. Most engineers do not wake up eager to think about backpressure or replication strategies. For many teams, it is something they take on only because their data grows large enough that they have no choice.

Working with large volumes of data can also be surprisingly hard. Even simple questions like “how many people viewed this product yesterday” can be slow or expensive to answer. Slight variations, such as “in the last minute” or “from customers in our loyalty program,” often require new pipelines or custom engineering. It is frustrating for teams, and it slows down decision-making.

Why Eventual

Eventual is the company behind Daft, an open-source Python framework for multimodal data processing. Our mission is to build generational technology that makes data processing across all modalities simple, reliable, and performant regardless of scale. When I learned what the team had built, I immediately recognized how much impact it could have on engineers trying to extract real value from their data.

What stood out to me about the team, besides their deep expertise in distributed systems and machine learning, was their emphasis on craftsmanship. Everyone cares about delivering a polished and dependable experience, even while moving at startup speed. It is rare to find a group that combines ambitious technical goals with such a strong focus on quality.

So when Sammy, our CEO, shared that many companies struggle to use their massive data lakes, the problem felt familiar and essential. And when I saw that Eventual had created a distributed query engine that lets product engineers build large-scale pipelines with just a few lines of code, I knew this was the work I wanted to do next.

Looking Ahead

I am thrilled to join the talented team at Eventual and help build the distributed systems that make complex data work simple for everyone else. If this kind of work excites you, we are hiring. And I would love to meet you.