Modern enterprises live and die by the strength of their data systems. What once was tolerated as a realistic margin of error is now seen as a substantial risk to business operations.
Someone who understands this shift is Ramesh Jitta, senior director and engineering lead at a Fortune 500 banking company, who has seen in real-time how data engineering went from a secondary support function into a central pillar of enterprise technology.
With more than two decades in software and data engineering, Jitta has built systems for financial services, healthcare, and enterprise compliance. Today, he discusses his philosophy for building lasting data platforms, one built on reliability, accountability, and testing above all else.
Building Improved Reliability
The first thing Jitta highlights is how, in the early 2000s, data engineering often relied on extraction-transformation-load processes that tolerated a certain degree of imperfection. If roughly 80% of records were accurate, the rest could be corrected manually. At the time, that level of reliability was generally considered acceptable. Today, expectations have shifted. Most enterprises now target 99.9 to 99.99% reliability, a standard shaped by the increasing use of digital channels to make transactions, tighter regulations, and rising customer expectations.
“Back then, if a few thousand records were missing, teams would fix it later,” he recalls. “Now, that’s simply not an option.”
The expansion of cloud computing played a central role in raising the bar. Scaling compute and storage no longer depends on on-the-ground servers or lengthy procurement cycles, making near-perfect reliability a realistic target.
For him, this means there are new and greater expectations for the performance of data, something crucial to continue building client trust. “Data quality isn’t something extra — it’s the foundation. Without it, everything else in the business is at risk,” he says.
Assembling High-Performing Teams
Another aspect Jitta focuses on is improving the internal workflows of the people in charge of building and then constantly expanding these systems. He rejects the notion of narrowly specialized roles where engineers simply take care of the coding aspects and hand off the rest of the work to testers or third-party support teams.
Instead, he champions what he calls “full-cycle engineers.” When he leads teams, he looks for people who can work with specific product requirements, design solutions, build code, write test cases, and support deployments. “I would hire one or two strong engineers over five mediocre ones — the delivery value is the same,” Jitta says.
This approach places cultural qualities on equal footing with technical skills. To him, dedication and perseverance are far more important than simply having talent. A high-performing engineer is one who, when faced with a problem, sticks with it until it’s solved, with the constraints that come with it.
Testing At Enterprise Scale
For Jitta, testing is both the greatest challenge and the greatest differentiator in engineering. Traditional ETL systems would often have to deal with incomplete or unrealistic datasets. By contrast, modern practices require simulation of real-world conditions before code reaches production.
This has meant building custom testing frameworks when off-the-shelf tools don’t cut it anymore. During one large-scale project that Jitta led, for example, his team needed to confirm that a real-time decisioning engine could process millions of daily customer interactions against federal compliance lists — all in under ten milliseconds — the frameworks in place couldn’t deal with the necessary load or latency conditions. His team spent three months designing its own system capable of simulating those demands.
Jitta also points out how working with synthesized and anonymized datasets also plays a critical role, as they give engineers the ability to stress-test pipelines with the type of information they’ll be working with but without exposing specific, private user information. The result is making sure systems can perform well under real transaction pressure while remaining secure.
His Thoughts On AI
Finally, Jitta sees AI as potentially helpful for improving manual processes, but he also remarks that it should come with careful and intentional applications.
More tools are coming out that can clear away repetitive work, and Jitta specifically mentions GitHub Copilot as an example of a tool that can automatically generate test cases with no manual input. But he cautions that these advances only work when grounded in real engineering principles, as AI can’t replace the specific skills and human savviness essential to debug elusive failures or the judgment needed to decide what makes a resilient architecture.
As a result, he believes clear frameworks need to be set as this integration goes on, specifically frameworks that include data quality as the bedrock of business reliability, governance as a safeguard against compliance risk, and a clear team culture as the multiplier that allows a small group of engineers to deliver at enterprise scale. “If we don’t have good data, you can’t make good business decisions,” he emphasizes.
Looking ahead, he believes engineering leaders must unify their platforms, integrating data sharing, policy enforcement, and security controls into centralized capabilities. That consolidation would not only reduce time-consuming tasks, but it would also put compliance and protection as key, non-negotiable factors.
A Blueprint To Keep Up With Enterprise Needs
Ramesh Jitta’s career reflects a simple but powerful principle: data must be both trusted and trustworthy. With engineers like Jitta at the helm, the next decade will belong to those who can understand deep technical complexity and can apply it to large-scale teams, building systems that scale in confidence as well as in size.
April Isaacs is a news contributor for DevX.com She is long-term, self-proclaimed nerd. She loves all things tech and computers and still has her first Dreamcast system. It is lovingly named Joni, after Joni Mitchell.
