Interview with Maksym Voloshyn: Engineering Challenges Unique to AdTech

Engineering in AdTech is unlike software development in most other industries. Behind every real-time auction, impression, and campaign is a complex technology stack that must process large volumes of data within milliseconds, while maintaining accuracy, reliability, and compliance. This way, success in AdTech is not about just writing efficient code with no errors – it is about designing systems that can deliver reliable performance under constantly increasing scale and complexity. 

However, this is a very basic description of how specific AdTech engineering is. To provide you with deeper insights, we interviewed Maksym Voloshyn, our Development Team Lead, and explored the challenges AdTech engineers face. Read on to learn more.

Thank you for joining us today! So, what differentiates engineering in AdTech from building software in other industries?

Advertisers and publishers choose programmatic technology not only because it allows them to automate some processes. Programmatic auctions happen within milliseconds, which enables them to streamline their media buying or monetization operations. However, this is also what differentiates AdTech engineering. Loads of things happen within those milliseconds. AdTech systems have to evaluate targeting and other rules, filter traffic, select demand partners, and determine the best response. If bid requests consistently require too much time to be processed, such a platform will simply not survive in the market. Engineers have to ensure the platform operates with minimum latency, and this process never ends. 

Another difference is scale. AdTech solutions process enormous volumes of traffic every day, so engineers also need to focus on the platform’s reliability and scalability. On top of that, the industry keeps evolving. New privacy regulations, market shifts, emerging ad formats, changing client requirements… Basically, engineering teams have to adapt their solutions continuously. 

“At Attekmi, we understand that AdTech is a field where you require not only deep technical expertise but also solid business understanding. That’s the only way to succeed in the industry that continuously evolves,” –

Maksym Voloshyn, Attekmi’s Dev Team Lead.

From your experience, which engineering problem looked simple but was actually difficult to solve?

In terms of AdTech platforms, analytics and reporting often look like the simplest capabilities you could imagine. A platform only has to collect the data and display it in its dashboards – sounds really easy, right? The problem is that AdTech solutions generate loads of events every second. These are bid requests, bids, impressions, clicks, conversions, and so on. Processing all this information in near real time while keeping reports fast, accurate, and consistent is a really challenging task.

The complexity increases because data comes from multiple sources. For instance, SSPs and DSPs may report the same events differently or with varying delays, which makes discrepancies almost inevitable. Engineers also have to ensure that reporting remains reliable even as traffic scales, since clients rely on these metrics to make business decisions and optimize campaigns.

Addressing these challenges requires a combination of robust data engineering and continuous monitoring. Engineering teams build scalable data pipelines that can process millions of events efficiently, implement validation mechanisms to detect inconsistencies, and use automated reconciliation to compare data across different systems. They also design reporting architectures that separate real-time and historical workloads, allowing dashboards to remain responsive without sacrificing accuracy. The main goal is to provide clients with reporting they can trust, even under massive traffic volumes and constantly changing market conditions.

In your opinion, can AI really improve AdTech engineering workflows, or is it overhyped?

I think AI is already delivering real value in AdTech, but only when it is applied to the right challenges and tasks. Let’s say, within engineering teams, it can help create documentation, write tests, identify potential issues before they reach production, and so on. AI does not really replace engineers here – instead, it allows them to spend more time solving complex architectural problems. 

However, whichever task you delegate to AI, I highly recommend reviewing everything it generates. Whether you use it for writing code or documentation, or for anything else, it can still make mistakes. AI is a powerful tool, but it still requires human oversight. Overrelying on it can be very expensive.

Beyond development, AI also has practical applications inside AdTech platforms themselves. For instance, machine learning models can detect unusual traffic patterns, improve fraud detection, and identify performance anomalies much faster than manual analysis. But again, AI is not a silver bullet. You should review every recommendation, especially in AdTech, where both performance and reliability directly affect revenue.

Programmatic is all about speed, but that is not the only crucial factor. How do you balance speed, scalability, and accuracy when every millisecond matters?

Well, finding this balance is among the biggest challenges in AdTech because these priorities actually compete with each other. Let’s say a system that performs very little processing can be extremely fast, but that may reduce the quality of decision-making. On the other hand, adding more business logic or analytics can significantly improve accuracy while increasing response time.

At Attekmi, we aim to optimize every layer of our platforms rather than relying on a single improvement. We invest in effective algorithms, asynchronous processing, intelligent caching, and scalable infrastructure. Besides, we continuously monitor performance to identify bottlenecks before they affect our partners.

In terms of the implementation of new features, we rely on the needs of our clients, as well as market changes and industry trends. However, every new functionality goes through performance evaluation and functional testing. In case a feature improves the platform’s capabilities but introduces, for instance, a risk of too high latency, we rethink its implementation. In AdTech, engineering is not only about making software work. You need to make sure that it works effectively at a massive scale without compromising reliability. 

Low latency seems to be a critical thing in AdTech. Why, and how can it be achieved?

In programmatic advertising, speed means everything, and so does low latency. Too slow responses affect everyone: marketers miss advertising opportunities while media owners lose revenue. Users may also feel frustrated – they may simply leave the website or app before the ad loads. 

Achieving consistently low latency requires optimization across the entire technology stack. For instance, engineers focus on minimizing network overhead, optimizing APIs, reducing unnecessary database queries, using efficient caching strategies, and writing highly optimized code. The important thing is that low latency is not achieved through one major innovation. It is the result of hundreds of improvements, careful architectural decisions, and constant performance testing. Maintaining those standards becomes even more challenging as the platform grows and new features are added.

We all know that privacy regulations keep evolving. How do they change the way engineering teams build AdTech solutions?

Privacy has fundamentally changed the way AdTech platforms are designed. Today, engineering teams have to think about compliance from the very beginning of the development process. Features must be built to respect user consent, minimize unnecessary data collection, protect sensitive information, and comply with different regulations across multiple markets.

Another challenge is that privacy requirements continue to evolve. Browser policies change, new regulations are introduced, and industry standards are regularly updated. That means engineering teams need flexible architectures that allow the platform to adapt without requiring complete redesigns every time a new requirement appears.

In many ways, privacy has become another core engineering requirement, as well as performance, reliability, and scalability. The goal is to build systems that remain effective while giving users and customers greater transparency and control over how data is handled.

From your perspective, which emerging technologies will have the biggest impact on AdTech engineering over the next few years?

AI will certainly continue shaping both engineering workflows and platform capabilities. Apart from helping developers, AI will play an increasingly important role in traffic optimization, predictive analytics, infrastructure monitoring, anomaly detection, and so on. I guess engineering teams will be able to identify problems even earlier and make systems more adaptive without increasing operational complexity.

Privacy-enhancing technologies will also become much more significant as the industry moves toward a future built around first-party data. Technologies such as data clean rooms will influence how platforms are designed and integrated.

From an infrastructure perspective, I expect cloud-native architectures, containerization, automation, and real-time analytics to become even more important. As traffic volumes continue to grow, engineering teams will increasingly rely on highly automated, stable systems that can scale efficiently while maintaining the performance standards that programmatic advertising demands. Finally, the biggest impact will come from technologies that help platforms become faster, smarter, and more adaptable without sacrificing transparency or reliability.

New episodes of our interview series are coming soon – stay tuned! 

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