Break It Down Right and It Builds Itself: Senior Software Engineer Oleh Hrebeniuk on Design Before Code

Oleh Hrebeniuk is a Senior Software Engineer with a track record in major European IT companies. He specializes in backend development, system architecture, and building high-load platforms. 

His project portfolio spans advertising technology, e-commerce, and SaaS products. He currently leads backend development for a no-code platform serving a European client.

We spoke about the discipline of thinking, the work that needs to happen before anyone starts coding, and the true cost of poorly considered decisions.

Oleh, you originally planned to study journalism and international relations. How did you end up in software engineering?

A happy accident. Computers have always been my hobby, but I never thought of them as a career. I was drawn to communication, travel, and understanding how the world works. Things didn’t go as planned, and I was offered a spot in the systems analysis program instead.

I accepted without much enthusiasm — and then discovered that I genuinely enjoyed taking complex systems apart and understanding how they fit together.

The funny thing is that everything I was looking for in journalism actually materialized: systems thinking, working with international teams, a career in a global market. The form turned out to be different, that’s all.

Your approach includes a strict cycle: define the task, plan, implement, verify. When did this become a consistent method?

When I saw what the opposite costs. Most engineers skip the first two steps and compensate by rewriting code later. I learned to invest in design upfront: I decompose a task into independent parts, think through the contracts between components and the integration points before the first line is written.

A well-decomposed task nearly builds itself — you can execute it in parts, in parallel, delegate it without losing structural integrity.

Can you give an example where this approach produced measurable results?

On an advertising technology project, we needed to speed up search across a dataset of more than two terabytes. The existing database couldn’t keep up.

I designed and deployed a full-text search cluster integrated with an analytical data warehouse. Synthetic benchmarks showed an average speedup of 50-60 percent; for certain query types, the improvement was up to fivefold.

How did you measure the result — synthetic benchmarks or production traffic?

Synthetic benchmarks using comparable queries before and after. Production traffic confirmed the order of magnitude, but precise measurements in a live environment depend on too many variables. Synthetic testing gives a clean picture, which is why I rely on it.

You’ve spent the last three years building a no-code platform. What was the hard part from an engineering standpoint?

The brief sounded simple: let users create applications without writing code. The engineering challenge was keeping the system flexible with a minimum number of components.

I was responsible for the entire backend architecture and effectively served as the technical lead — my decisions shaped the direction of the frontend and the way the team organized its work. The result was three major contracts that the company secured because the required functionality was ready ahead of schedule.

Three contracts — that’s significant. What’s the connection between architectural decisions and a commercial outcome?

A direct one. When the architecture is well thought out, you can add functionality fast. The client sees a working product, not promises. In our case, we were meeting the client’s business requirements ahead of their own timeline, and that created the conditions for closing the deal.

You’ve said that the depth of the process should match the task. How does that work in practice?

Rigor should be proportional to the cost of failure. In some cases, the right move is to build a quick prototype and test a hypothesis with real users. In others, you need strict architecture and reliability requirements.

I don’t apply the same process to every project. Dogmatism in either direction — “move fast at any cost” or “process for the sake of process” — is equally harmful. The risks, the stage of the product, and the consequences of an error should determine the depth.

How do you handle code review — both for other people’s code and your own?

The same way. Every piece of code, regardless of who wrote it, goes through the same review, testing, and architecture requirements. A result is only final once it’s been verified. I don’t make exceptions for my own code. If anything, I’m harder on it — I know where I might have taken a shortcut.

So decomposition is also a way to manage a team?

Exactly. When a task is broken into independent parts with clear contracts, people don’t need to coordinate every step.

Everyone understands their piece, knows what input they need and what output they deliver. That reduces the communication overhead and makes parallel work possible.

What type of engineering mistake do you consider the most expensive?

A misunderstood task. A bug can be caught by a test. An architectural miscalculation can surface during review. But if you spend a month building something nobody needs, no process will recover that time. The most expensive loss in software development is well-built unnecessary work.

Where do you think the industry stands right now in terms of engineering culture?

We’ve built a rigorous discipline around code: reviews, tests, CI, architectural standards. Yet in most teams, decisions about what to build and why are driven by intuition or deadline pressure. Those decisions determine where years of engineering effort will go. 

I’d like to see the same rigor applied to judgment as we apply to code: evidence-based reasoning, documented logic, and the ability to go back and ask — why did we decide this, and were we right?

Why a Slow Start Produces Fast Results

Over the course of his career across several international IT companies, Oleh Hrebeniuk has worked with high-load systems, advertising platforms, and SaaS products. 

His engineering signature is design before implementation, decomposition as a management tool, and verification as a mandatory step without which no task is considered complete. In an industry where speed is often valued over clarity, that approach looks unusual — and that is precisely why it produces measurable results.