Meet Shehzad Mevawalla, EVP of Enterprise Data Tech
An interview with Shehzad Mevawalla delving into his long-standing technology career and vision for data and AI at Capital One.
This article captures a recent conversation with Shehzad Mevawalla, EVP, Head of Capital One’s Enterprise Data Technology organization. Shehzad joined Capital One in March 2025 with more than 36 years in data architecture, large-scale systems and AI, including roles at industry heavyweights Amazon, FICO and NCR/Teradata. Shehzad leads technology and engineering strategies for Enterprise Data, partnering closely with Capital One’s Chief Data Officer to continue to modernize and transform the company’s data ecosystem.
We caught up with Shehzad as he approaches his one-year anniversary with the company to learn more about what attracted him to Capital One, his priorities for data technology and its role as a critical enabler for AI, and personal leadership philosophies that inform how he leads high-performing enterprise technology organizations.
With a longstanding career in data and technology, what attracted you to this role at Capital One? How does it align with your professional goals and interests?
As I researched Capital One’s culture and history, I realized that Capital One is really a technology company that also happens to be a bank. The founding principles of the company are based on leveraging deep analytics and technological advancement to deliver new, unique and personalized experiences for customers. What also stood out was Capital One's relentless drive for reinvention. This isn't a company satisfied with the status quo; it constantly seeks to evolve. That commitment to innovation, coupled with a strong emphasis on employee well-being and development, made this role incredibly attractive.
During my long tenure at Amazon, I was extremely fortunate to work across a wide variety of areas with some amazing people. My experience there, particularly across large-scale data, risk management and machine learning, provided me with a highly relevant background. This deep alignment between my specific skills and experiences and the technical requirements and strategic vision of this role at Capital One was an excellent match. The connection was clear, and I'm genuinely thrilled to be here and to be contributing to Capital One's continued success.
What have been your top priorities in your first year with the team?
Joining Capital One as the head of Enterprise Data Technology at such a pivotal time is incredibly exciting. The company has already made impressive strides in consolidating its operational and analytical data into a single, high-quality and well-managed data platform. This multi-year journey, spanning data collection, verification, publication, storage and usage, has been executed with remarkable care and insight.
My absolute first priority has been to connect with people, which includes building strong relationships with my teams, our partners across the organization and our customers. Concurrently, I've dedicated significant time to learning our platforms and services, and the underlying data itself—how it's structured, managed and ultimately used to drive value. This dual focus on people and technology ensures our organization can lead effectively from an informed and collaborative position.
I’ve also been focused on examining everything we do through the lens of the customer. This includes identifying the many strengths of our current system so we can amplify and double down on those successes. Equally important is uncovering any gaps or points of friction that might hinder efficiency or lead to missed opportunities, then quickly addressing these areas to enhance our overall effectiveness.
Looking ahead, a high priority for me is to further increase the footprint of AI within our data systems. My goal is to make our data truly conversational, enabling more intuitive and powerful interactions for our users. This involves leveraging advanced AI techniques to unlock new insights, automate processes and create a more dynamic and accessible data environment for everyone.
Over the course of your career, what has changed about the way companies use data and how they think about managing and leveraging data, especially to power their AI aspirations?
The way companies use data has significantly evolved over time, especially with the rise of AI. As I think about it, three major changes have driven this evolution:
- The exponential growth in data scale. The shift from traditional databases to cloud-based technologies gives companies unlimited and cost-effective storage and compute capabilities.
- The capability potential of unstructured data. Previously, unstructured data was largely inaccessible for decision-making without expensive processing. However, new models have made it queriable, allowing it to be incorporated into business decisions.
- This combination of structured and unstructured data capabilities represents a revolutionary shift, opening new frontiers for data utilization and value creation that were previously unattainable.
You’re a software engineer by trade. Can you elaborate on how that training has manifested into the way you think about the systems, software and the developer experience today?
My training as a software engineer is at the core of how I approach systems, software and the developer experience today. Leveraging both foundational technical thinking and deep understanding of the journey from concept to production allows me to make informed decisions and connect complex ideas, even when navigating new technological frontiers.
The developer experience is currently evolving as new models for code generation signal a fundamental shift in how systems are built. Although these tools are in their early stages, they promise to significantly enhance productivity and accelerate engineering throughput, helping to transform ingenuity into tangible products. Throughout this evolution, core development expertise and engineering proficiency will remain essential.
Can you share your most important learnings from leading speech and audio technologies for Alexa?
When I began my journey with Alexa, I was new to the highly specialized and technical field of speech science. To succeed, I had to quickly learn and adapt, embracing new frontiers through a mix of curiosity and confidence in my ability to master complex technical knowledge. The most significant milestone in this role was our work on moving from traditional machine learning to speech LLMs. In doing so, we delivered truly conversational and human-like interactions for Alexa and then further generalized it for other voice-based applications as well.
I’m excited that we’re now applying similar approaches to achieve natural-language interactions across the business at Capital One. This is especially true as some of the most exciting trends in data technology right now revolve around unstructured data and the increased accessibility of data use through AI. AI-powered data analytics tools are emerging that can generate datasets or answers to questions, which promise to revolutionize data management and utilization. This is a key area of focus for us as we leverage the power of foundational LLMs to build AI into our work every day. In doing so, we’re continuing to amplify the huge value of data by making it easily accessible and usable through a natural and intuitive interface for both businesses and customers.
Finally, are there any other notable career highlights and/or your passions outside of work you’d like to share?
Professionally, every career change I’ve made has been to very different areas and has always been about expanding my own learning or expertise. At Amazon, for example, I took on roles in supply chain as well as speech, both of which are highly technical fields that had a steep learning curve for me. As you go through your career, keeping an eye on interesting areas you’d like to learn more about will enhance your overall understanding and personal development. At Capital One, I am learning all about the banking industry and how Capital One serves its customers. There’s no better way to do that than by understanding the data behind it, which gives you a really well-rounded view.
Personally, I have been a big car enthusiast since I was very young, and my love for cars has continued to this very day. I continue to spend my off-hours, when not with my children and family, enjoying this hobby.
