With the advent of cloud technology, the old playbook of defining data architecture has been fundamentally challenged. “In this cloud-first world, data architectures have evolved from using standalone or local database clusters to complex, multi-region, distributed, and more resilient platforms, says Sameer Jog, VP – Data Architecture, Fidelity Investments India, in an interaction with Enterprise Times, underscoring the imperativeness of aligning data architectures to data strategy for long-term business value.
Enterprise Times: You’ve spent over two decades in data and technology roles. How has your experience shaped your approach to data architecture and leadership?
Sameer Jog: I have been a data professional for 27 years, working across roles such as data engineer, analyst, data modeller, and data architect. Each role and project has added unique insights that have contributed to my career journey. My generation has witnessed paradigm shifts in applications architecture, from simple two-tier systems to complex, real-time, multi-cloud, platforms operating 24/7. Likewise, data architectures have evolved from rigid monolith platforms designed mainly for record-keeping to today’s distributed, fault-tolerant platforms that service a complex myriad of use cases spanning from operational functions to advanced analytic payloads driving business decisions. Data is literally the fuel that is driving these business decisions.
In my experience, successful data architectures hinge on sound data models, well-engineered data pipelines, consistent taxonomy, and robust support frameworks that scale for the future. Automation plays a key role in today’s solutions, ranging from DevOps to platform observability, predictive models, and everything in between.
Enterprise Times: How is the role of data architecture evolving in the age of AI, cloud, and data democratization?
Sameer Jog: The old playbook of defining data architecture has been fundamentally challenged in recent times. The advent of cloud technology has changed the game. Cloud has offered a cost-effective way to experiment, innovate, and sometimes fail fast, creating a level playing field.
In this cloud-first world, data architectures have evolved from using standalone or local database clusters to complex, multi-region, distributed, and more resilient platforms. Data pipelines can flow seamlessly from old classical files based on batch loads to real-time event-driven integration patterns.
However, some fundamentals remain unchanged. The need for a traditional, robust data model with well-defined taxonomy is more critical than ever. Similarly, secure designs that enable well-engineered solutions continue to be the cornerstone of success.
Enterprise Times: You describe yourself as a hands-on data architect with a strong focus on delivery and domain. How do you balance strategic vision with execution at scale?
Sameer Jog: I see strategic vision and hands-on execution as complementary rather than competing priorities.
It starts with defining the data strategy, which serves as the foundation for creating architectural blueprints across groups.
Being hands-on provides deep insight into one’s business domain. The better one understands the problem, the more pragmatic one’s solution becomes. A strong delivery focus keeps one grounded and helps define solutions that are practical, immediate, and can contribute to the team’s long-term goals.
Building robust data architecture capabilities requires a sustained commitment and investment. This role sits at the intersection of analysis, design, and engineering, demanding not only technical expertise but also strong domain knowledge.
Enterprise Times: What are some key lessons you’ve learned about enabling teams to deliver consistent, high-quality data outcomes?
Sameer Jog: In my experience, teams perform best when both functional and non-functional requirements are clearly understood from the outset. Collaborative designs and architecture discussions, along with a collective brainstorming of test cases, create alignment and strong ownership. Defining architecture should always be a collaborative process with the appropriate stakeholders. Aligning data architectures to data strategy is essential for long-term effectiveness.
A common challenge is when a team focuses only on the functional requirements and not the non-functional requirements such as performance, support, production stability, availability, and disaster recovery. Sharing clarity on such aspects upfront during the design phase drives teams towards long-term, optimal, and sustainable solutions.
Enterprise Times: What emerging trends do you see in the areas of data architecture and data engineering?
Sameer Jog: Conventional databases are being challenged by efficient, standards-driven distributed databases that offer strong resilience. Modern data architectures are evolving in real-time and have event-driven processing built in. This exponential growth of data has resulted in several cloud-native big data platforms joining the mix. These platforms often come with embedded analytics capabilities that have many applications, such as real-time monitoring and the ability to detect anomalies. The space is truly evolving fast and is always exciting.
Enterprise Times: What advice would you give to young data professionals aspiring to build impactful careers in the space?
Sameer Jog: To quote Steve Jobs, “Stay hungry, stay foolish.” Opportunities are endless. However, it is your creativity and appetite to learn that will influence how you much you will grow in your career.
I would also like to add that technology, while always evolving, is still just a means to an end. The real art of problem-solving needs an in-depth understanding of the business domain and critical design thinking. So don’t just learn technology. Also learn the domain.
