Retail runs on data. Major brands rely on thousands of data points to predict what customers want, determine how much inventory to keep in each store, and more. Despite endless dashboards and AI hype, many companies still make decisions solely based on siloed or outdated information. That’s where Shriya Agarwal comes in.
With over nine years of experience in data engineering and strategic consulting, Agarwal influences how global retailers utilize data. She analyzes the past to help companies act smarter in the present.
However, Agarwal is not interested in building flashy tech. Instead, she focuses on what works. In Industries where complexity often breeds confusion, she simplifies the process. As a result, Agarwal is part of a growing group of engineers who believe data should serve people.
The Problem With Scale
Global retailers face a unique challenge. A single decision about supply chain logistics can impact millions of customers, hundreds of vendors, and entire regions. However, the systems that support these decisions are often disconnected, bloated, or built for a version of the business that no longer exists.
Agarwal understands the frustration of this disconnect. Before joining her current role as a senior data engineer at a major international retail and e-commerce company, she served as the youngest Principal Consultant at a supply chain technology firm. There, Agarwal led complex transportation management implementations for some of the world’s largest consumer goods companies.
She noticed repeatedly that businesses had the data but didn’t trust it, or in some cases, didn’t know how to use it.
Agarwal’s approach was to make the data usable. She began building systems that transform raw information into actionable insights. This method enables people to make informed decisions.
From Curiosity to Career
Agarwal’s fascination with data started long before it became a buzzword. As a student, she gravitated to the idea that information, when structured correctly, could solve real-world problems. That drive hasn’t changed.
Agarwal has led some of his most ambitious initiatives at her current company. She helped migrate over 3,500 Hadoop workloads to the Google Cloud Platform across eight International markets. That was an operational reset. Agarwal laid the groundwork for faster decision-making, sourcing, inventory, and customer analytics by creating more scalable, reliable data pipelines.
Her fingerprints are on projects like Geo Demand Placement and Scintilla. Both projects help global teams predict demand, optimize sourcing, and reduce waste. These aren’t just theoretical wins. Agarwal’s work has resulted in more accurate forecasts, fewer stockouts, and more responsive retail experiences for millions of customers. In other words, she makes data practical.
Rethinking What a Data Engineer Does
Many engineers are builders. Agarwal is both a builder and a translator, which sets her apart. Whether designing inbound logistics systems or developing predictive models for store sales, she brings a business-first mindset to every project. This engineer goes beyond writing code to ask questions like: How will this help someone in the supply chain make a better call? How will this reduce friction for the customer?
“That dual lens allows me to deliver solutions that are technically sound but also impactful in terms of business value,” Agarwal says.
This strategic thinking is one reason why she’s currently pursuing an MBA in Strategic Management.
That dual focus has already paid off. Agarwal’s systems have helped improve data quality, cut operational costs, and increase agility across large, multinational teams. But perhaps more importantly, she’s built trust in the data itself. Agarwal’s credibility makes people more likely to use the systems she designs.
Data That Thinks Ahead
Agarwal’s latest work is focused on AI-powered data systems.
“AI has become the missing link,” she explains. “It’s enabling us to go beyond analysis into action, turning insights into automation, prediction, and personalization.”
But Agarwal is careful not to treat AI as a silver bullet, so she spends a lot of time getting the foundations right.
Once the groundwork is in place, the results are powerful. Agarwal has developed predictive models that help retailers optimize inventory across geographies. These models consider store-level sales, logistics constraints, and external signals like regional events or weather.
The impact? Reduced overstock. Fewer out-of-stock alerts. Happier customers. And Agarwal is not stopping there.
She’s also built real-time dashboards and personalized recommendation systems that improve brands‘ engagement with shoppers. Agarwal’s future vision is to make retail smarter, faster, and more human.
When Data Meets Empathy
What makes Agarwal’s work different isn’t just her technical skills and values. At a Women in Tech Hackathon, she led the creation of a personal care assistant bot designed for dementia patients. It was a passion project, and it won. But more importantly, it reflected Agarwal’s belief that data and AI can—and should—be used to improve lives.
Agarwal is now exploring how her skills can be applied to public services and healthcare. She uses the same principles she brings to retail: to make systems people can rely on, reduce friction, and make sense.
This empathy-driven approach is also why Agarwal has become a respected voice in the industry. As a speaker, mentor, and judge at International Tech conferences and awards, she advocates for more diversity in data and more purpose in tech. Agarwal doesn’t just want more women in STEM. Instead, she wants more meaningful work across the board.
Looking Ahead
Shriya Agarwal aims to keep building tools that help businesses think better and act faster while staying grounded in purpose.
Whether she’s leading a global cloud migration, mentoring young women in tech, or designing AI systems that improve decision-making, her approach is the same: stay thoughtful, stay strategic, and always ask how the work can create real value. She has shared the stage with CEOs and senior leaders at the Women in Technology of Northwest Arkansas Conference, standing out as a young innovator with bold ideas in AI.
In a field often dominated by hype or abstraction, Agarwal is a welcome exception. As the demand for global data systems grows, so does the need for leaders like Agarwal; people who can build what’s needed and question what’s not. Her career may be just beginning, but her impact shows companies how data is a tool for real progress.
