In the world of high-stakes finance, where assets under management can soar into the hundreds of billions, the margin for error in data is zero. We sat down with Kostiantyn Shkliar a QA Automation Architect with over 13 years of experience, to discuss how he transitioned from traditional quality assurance to architecting mission-critical validation engines for $130B+ AUM environments.
You’ve spent over a decade in the IT industry, moving from major firms like EPAM to high-stakes FinTech. How has your perspective on data integrity evolved during this time?
Early in my career, the focus was often on the user interface, making sure the buttons worked and the flow felt right. But as I moved into the investment sector in London and eventually to major financial institutions in the U.S., my perspective shifted entirely. In a high-load environment, the UI is just a thin veil. The real battle for reliability is fought at the architectural and data levels. Today, I don’t see myself as someone who finds bugs, but as an architect who proactively defends the integrity of the data that drives global finance.
You are known for developing a “Hybrid Data Validation Engine” that reduced a validation cycle from 48 hours to just 2 hours. What was the core philosophy behind that shift?
The core philosophy was White-Box Validation. Traditional UI-based testing is notoriously fragile in dynamic cloud environments like Salesforce; it’s slow and prone to false positives. By bypassing the interface layer and interacting directly with APIs, Metadata, and the database using .NET 8.0 and C#, we removed the latency of the browser. We moved from testing “how it looks” to verifying what it is. Reducing that cycle time wasn’t just about speed, it was about enabling daily production releases with absolute confidence that $130 billion in assets remain secure.
In the current market, we see a massive push toward Security-as-Code. How are you integrating security into the actual fabric of the development pipeline?
Security can no longer be a final check before release. In the environments I manage, we embed automated security audit mechanisms directly into the CI/CD pipelines. This includes verifying complex permission sets and security models via SOQL and automated scripts. If a configuration change inadvertently opens a data vulnerability, the pipeline fails immediately. We treat security permissions as code that must be validated with the same rigor as a financial transaction.
High-fidelity modeling is critical in FinTech. How do you use data factories to simulate the complexity of a $130B+ environment?
You cannot test a high-stakes system with dummy data. I develop scalable Data Factories using JSON schemas that allow us to model complex financial environments with high fidelity. These factories generate the volume and variety of data needed to stress-test the system’s logic and architectural limits without ever touching sensitive production information. It’s about creating a perfect digital twin of the financial ecosystem.
Looking ahead, what do you believe is the next frontier for data management and quality control in financial services?
The next frontier is undoubtedly AI-driven autonomous quality control. We are moving toward systems that don’t just follow a script but are capable of self-detecting anomalies within the data architecture itself. My goal is to pioneer systems that can observe data patterns and say, “This relationship shouldn’t exist,” before a human even realizes there’s a risk. Complexity is the ultimate enemy of reliability, and AI is the tool that will help us manage that complexity.
You often say that “Automation is not an expense—it is business insurance.” What is your advice to organizations looking to scale their data operations today?
Many organizations see validation as a bottleneck, but it is actually the primary enabler of scale. My advice is to stop focusing on external bugs and start defending your architectural integrity. If you want to scale without compromising quality, you must invest in intelligent validation mechanisms that allow you to move fast without breaking the foundation. In FinTech, your data accuracy is your reputation; you should protect it with the same intensity you use to grow your assets.
