In recent months, the attention of the IT community has focused on the unpredictability of failures in large systems and the critical role of observability – not just as a monitoring mechanism, but as a tool for preventing and analyzing failures in real time. So, in January 2026, Verizon, the largest American telecom operator, had a large-scale network outage, as a result of which users across the country faced the loss of mobile communications and mobile Internet for hours. Thousands of complaints from customers were recorded on services such as Downdetector, and the cause of the breakdown was not officially announced for a long time.
Earlier, in December 2025, Cloudflare's global cloud infrastructure experienced a major outage that affected services like X, Spotify, Uber, Canva, and ChatGPT for several hours due to a glitch in the database access control system – this led to million errors worldwide and once again highlighted how critical transparency is to internal mechanisms of large distributed systems.
As a result, such events became an indicator of a system problem: with increasing complexity of architectures, traditional monitoring no longer allows for continuity of service and quick identification of root causes of failures. It is in this context that the practical experience of engineers who work with high-load platforms in conditions of constant growth is especially valuable. Gleb Shkriabin, CTO of the American IT company WhiteCodeWorks LLC and a distributed systems engineer with more than a decade of experience in mission-critical environments. In recent years, his architectural solutions and analytics systems have reduced database response times by up to 70%, infrastructure costs by 20%, reduced fraudulent activity, and ensured platform stability even during peak periods. These led to recognition as he won Time for Innovation Award 2024, American Business Expo Xmas Award 2025, became a Senior Member of IEEE, and a judge in Digital Leaders Awards 2024. Gleb’s experience directly answers the key question that worries the market today: how to make the growth of a digital product manageable when scale stops forgiving architectural mistakes?
In systems that process tens of millions of requests per day, architecture quickly ceases to be an abstract engineering choice. Any decision, from the service interaction scheme to the data management model, begins to affect the speed of releases, the number of errors, and the ability of the platform to scale without loss of quality.
In the projects of Involta LLC, where Gleb Shkriabin was responsible for architecture and technical leadership of the teams, these connections were especially evident. AdTech platforms and analytics were working under a constant increase in workload, and the key task was not to "accelerate development", but to stabilize the system when scaling. As a result of architectural changes and process revisions, specific effects were recorded: about 20% annual revenue growth, 15% growth in the customer base, 60% reduction in development errors, acceleration of release cycles by 50% and an increase in team productivity by 40%. At the market level, this resulted in a 30-40% increase in the company's share over the period of operation.
These indicators are important because they show that in high-load systems, architecture is not an internal technical layer, but a factor that directly affects the commercial result.
As the scale increases, standard monitoring stops coping with its task, metrics capture the fact of the problem, but do not explain exactly where the risk is accumulating. In distributed architectures, this leads to delayed reactions and costly incidents.
While working at BrainRocket LTD, an international IT company in fintech and iGaming, as a Software and System Engineer, Shkriabin worked on systems that process up to 100 million requests per day. Under these conditions, he developed and implemented an analytical monitoring system for highly loaded databases, focused on query behavior, load patterns, and early detection of anomalies. Technically, the solution combined analysis of runtime metrics, query management, and architectural changes aimed at reducing latency and load isolation.
The key to Gleb Shkriabin's approach was to shift the focus from abstract optimization to measurable results: “I was directly responsible for the development and support of platforms that process tens of millions of requests per day. And since the workload is very high, it helped me to make progress by constantly striving for measurable results – reducing response time, infrastructure costs, increasing system stability and supporting business growth with specific technical solutions.”
According to this approach, the results were recorded in specific figures: a 70% reduction in database response time, a 20% decrease in infrastructure costs, a 35% cutback in fraudulent activity, and 99.9% maintenance of system availability during peak traffic periods.
Over time, this led to industry recognition, including winning the Time for Innovation Award in the “Technical Innovation of the Year” nomination in the field of IT and digital technologies in 2024, which was an external confirmation of its applied value.
As engineering solutions begin to affect the sustainability of large digital products, the form of professional recognition is also changing. It is not about popularity, but about trust – in judgments, architectural solutions, and the ability to evaluate other people's projects.
Gleb Shkriabin joined the expert council of the Digital Leaders Awards 2024, a national-level award recognizing leading digital products, platforms, and professionals in technology and digital transformation, where he evaluated projects in the Developer of the Year, Development of the Year and Platform of the Year nominations. Among the participants are large national companies and infrastructure players, including banks and technology corporations. In November 2024, he was accepted as a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE), a professional community where this level of membership requires proven experience, significant achievements, and recognition from colleagues.
“Over time, I increasingly had to make final technical decisions on complex systems with a high degree of risk, which imposed more responsibility, but also gave me experience. My recommendations were accepted as authoritative and used as the basis for corporate-wide technical standards, rather than for individual solutions,” Gleb Shkriabin commented.
Gleb's professional achievements were recognized by the professional community and these are the reasons for his joining the Hackathon Raptors. The format of this community involves evaluating not concepts, but practical solutions that have been tested in the production environment, which directly correlates with Shkriabin's area of specialization – highly loaded and mission-critical systems.
An additional confirmation of the applied significance of his engineering developments was the Tech Leader of the Year award in the Software Development category at the American Business Expo Xmas Award 2025. This competition is aimed at technology leaders whose solutions demonstrate a measurable effect on business and industry, rather than experimental or exploratory in nature. And such leaders should pass on their knowledge to future generations in order to expand the number of highly qualified specialists in the field. For example, Shkriabin has published a number of papers on practical aspects of distributed systems and microservice architecture, including topics such as CQRS, event-driven architectures, distributed transaction management, and data integrity. These texts were published both in scientific publications and in professional IT media and were used as applied cases rather than theoretical reviews.
Experience with high-load systems shows that resilience today is formed not at the time of an incident, but long before it – in architectural choices, the approach to data, and the ability to see the limits of growth in advance. It is this shift that explains why engineering expertise is increasingly becoming the subject of external evaluation and professional trust, rather than just internal recognition within teams. Against the background of the transition of observability to a predictive model and the increasing requirements for the reliability of digital products, such practices increasingly determine not only the technical quality of systems, but also their economic viability.