Breaking Barriers: Paras Pandey’s Journey From India To Working On Scalable Data Systems At Amazon

For many, leading a logistics company in India might look like a lifelong career in operations and management. But for Paras Pandey, it became the starting point of a journey that would eventually take him to Amazon, where he now works as a Data Engineer designing scalable data systems that process petabytes of information daily. His story is one of resilience, adaptability, and a relentless curiosity about how data can transform the way businesses function.

Paras explains that his career, whether as a CEO in logistics or as a data engineer in the United States, has always been about one central goal: using data to solve real problems.

Early beginnings in India

Paras’s professional journey began as the CEO of a courier and logistics firm in India. The role immersed him in decision-making, operations, and strategic planning. Yet, amidst these challenges, one insight stood out: the power of data. He realized how impactful data could be on operations. Even the smallest insights, such as delivery times, route optimizations, or customer behaviors, could significantly transform efficiency.

This discovery became the spark that pushed him to pivot toward technology, a shift that would define the rest of his career.

Exploring machine learning

The turning point came when Paras joined Bellwethr as a Machine Learning Consultant. The role gave him an opportunity to work closely with students, helping them apply machine learning models to practical challenges. This experience demonstrated how theoretical concepts could be transformed into solutions for real-life problems. It reshaped his thinking and reinforced the idea that data was not just about numbers, but about creating real-world impact.

This phase deepened his conviction that data could drive change and laid the foundation for his transition into engineering roles.

Engineering data pipelines

At Analog Devices, Paras designed ETL pipelines in AWS Glue for the Marketing Analytics team. The challenge was clear: to help optimize product pricing processes using reliable and structured data. Here, he learned the importance of clean and reliable data. Structured data enabled better decision-making and showed how pipelines could bring measurable business value at scale.

This role helped him sharpen his expertise in cloud technologies and cross-functional collaboration, preparing him for his next leap.

Building at scale at Amazon

Today, Paras works as a Data Engineer at Amazon, where scale is both the challenge and the opportunity. From logistics to sales and marketing, his systems process petabytes of data daily. Every system has to be fast, reliable, and scalable.

To meet these demands, he has built automated ETL pipelines, event-driven systems with Apache Spark and AWS Lambda, and monitoring solutions powered by CloudWatch. His focus remains on creating architectures that are not only efficient but also adaptable and future-proof. For Paras, scalability is not just about handling more data; it is about designing systems that can support growth for years to come.

Lessons for the future

Looking back, Paras views his journey not as a straight path but as a series of pivots driven by curiosity and resilience. From running a logistics company to exploring machine learning and now managing large-scale data systems at one of the world’s biggest companies, his path underlines the value of adaptability.

He believes his career is proof that professionals do not need to remain confined to a single role or industry. With determination, curiosity, and a willingness to embrace challenges, it is possible to transform a career and make an impact on a global scale.

For Paras, the future is not just about building data pipelines. It is about using technology to make businesses smarter and more resilient. His journey, filled with leaps across industries and roles, serves as a reminder that every challenge can also be an opportunity.

Published on: Tuesday, November 11, 2025, 08:26 PM IST