Introduction: A Career Built on Foundations and Futures
Seventeen years ago, I stepped into the professional world through the gates of Tata Consultancy Services (TCS)—a company that laid the groundwork for my understanding of large-scale IT systems and global enterprise transformation. Over time, I evolved into a leader in the data space, helping clients make strategic decisions with insights powered by data.
But what truly shaped my journey was not just technology—it was the power of intergenerational collaboration. My experience took a new dimension when I moved to Infosys, where I was exposed to an ecosystem that celebrated cross-generational teamwork and co-innovation.
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Chapter 1: The TCS Era — Laying the Bedrock
At TCS, my first role was in enterprise application support, dealing with data from legacy systems and on-prem ERP platforms. Over the years, I shifted into more data-centric roles:
• Data Warehousing using Informatica and Oracle
• Building ETL pipelines before the term “Data Engineering” became mainstream
• Leading BI and reporting teams across retail and BFSI sectors
Here, I gained a deep appreciation for data governance, data quality, and the business value of well-structured information. I worked with senior architects who had 30+ years of experience. Their structured thinking and risk-averse strategies gave me the patience to architect with precision.
But the real learning came from mentoring younger professionals, many of whom brought fresh enthusiasm and questioned traditional processes. It was in these moments that I realized the strength of intergenerational collaboration: I provided the framework, they brought the agility.
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Chapter 2: The Inflection Point — From Traditional BI to Modern Data Stacks
After more than a decade, I felt the itch to explore cloud, AI, and real-time analytics. That’s when I moved to Infosys, attracted by its push toward data modernization and AI-first mindset.
Here’s how things changed:
• Moved from traditional SQL-based reporting to modern cloud-based data stacks like Snowflake, Databricks, Azure Synapse, and Google BigQuery
• Led data lake initiatives for telecom and energy clients
• Drove adoption of MLOps and AI governance frameworks
At Infosys, teams were more diverse than ever—both in age and skill. I was often paired with Gen Z data scientists, who were fluent in Python, PyTorch, and AutoML platforms. I brought in experience around:
• Data lineage and integrity
• Business continuity in migration
• Navigating regulatory landscapes (e.g., GDPR, HIPAA)
Our collaboration was symbiotic. One team, many perspectives.
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Chapter 3: A Real Story — The Intergenerational Project That Changed Everything
One of the highlights of my career came during a data transformation project for a global consumer goods giant.
The challenge: Migrate 15+ years of siloed data into a unified analytics platform within 10 months.
The team:
• Myself (Lead Architect) with 17 years of experience
• A 55-year-old data SME who had been part of the client’s legacy setup since 2005
• A 28-year-old data scientist focused on customer segmentation using AI
• A 23-year-old engineer working on building dashboards with Power BI and dbt
The turning point: When we hit a roadblock with messy product master data, our legacy SME knew why the data was broken—something only someone with historical context could spot. The youngest member created a transformer-based model to clean and harmonize data at scale. I tied it all together, architecting the flows and ensuring scalability.
Outcome: We delivered the platform two weeks ahead of schedule, with over 200 curated KPIs and near real-time business monitoring. The client rated it their most impactful data initiative of the year.
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Chapter 4: Lessons from 17 Years of Experience
1. Technology evolves. People evolve faster.
– What worked a decade ago won’t work now. But people, with the right mindset, can bridge any tech gap.
2. Intergenerational collaboration is not optional—it’s a superpower.
– Experience offers stability. Youth offers speed. Together, they unlock innovation.
3. From ETL to AI: Stay curious.
– My move from Informatica to Spark, and now to AI observability tools, happened because I kept learning.
4. Empathy and mentorship are key.
– At both TCS and Infosys, the projects that stood out weren’t just technically sound—they had empathy and trust between team members.
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