From Mumbai to New York: How I Built a Career in Data Engineering That Touches Millions of American Lives
From Sleep Apnea to Supply Chains: How One Data Engineer Built Infrastructure That Saves Lives
I never planned to build data infrastructure for trauma surgeries.
I never planned to help thirty million Americans breathe better at night.
I never planned to build systems that track life-saving medical devices from manufacturing facilities to operating rooms across the United States.
But that is exactly what fourteen years in data engineering—built on a foundation of curiosity, persistence, and a willingness to do the unglamorous work that makes everything else possible—has led me to.
I am Priyanka Shelar. I grew up in Mumbai, India, in a family that believed deeply in education and even more deeply in hard work. I earned my undergraduate degree in Information Technology, then a Master’s in Software Engineering from BITS Pilani—one of India’s most competitive engineering institutions. Then I made the decision that would define the next chapter of my life: I came to the United States to build a career in data engineering.
What followed was not a straight line. It never is. But it was a journey that taught me more about resilience, reinvention, and the quiet power of doing technical work that genuinely matters than any career plan could have predicted.
The Work Nobody Sees—and Why It Matters
Data engineering is invisible work.
When a doctor sees that a patient’s sleep therapy compliance has dropped and makes a call that prevents a hospitalization, nobody thinks about the data engineer who built the pipeline that made that insight possible.
When a hospital receives the surgical device it needs for a trauma procedure on time, nobody thinks about the engineer who built the supply chain data infrastructure that tracked that device from manufacturer to operating room.
When a Fortune 500 company makes a marketing investment decision that creates jobs and drives economic growth, nobody thinks about the engineer who built the analytics platform that informed that decision.
We are the invisible layer. The foundation. The infrastructure that makes everything else possible.
And I have spent fourteen years building that infrastructure—not because it makes me famous, but because I genuinely believe that data engineering, done well and applied to the right problems, is one of the most important contributions a technical professional can make to the world.
The Moment That Changed Everything
My first major project in the United States was working on a healthcare informatics platform for one of the world’s leading sleep therapy companies.
Sleep apnea affects more than thirty million Americans. Left untreated—or treated with a device the patient stops using—it contributes to heart disease, stroke, diabetes, and premature death. The clinical challenge is not the therapy itself; the therapy works. The challenge is keeping patients on it.
Our team built a platform that could predict—with approximately 95% accuracy—which patients were at risk of discontinuing therapy before they actually stopped. Clinical staff could see their entire patient population’s compliance status in real time, reach out proactively, and intervene before health deteriorated.
I remember the moment I read feedback from a client stakeholder after the platform launched. She wrote that Territory Managers, Solutions Consultants, and Regional Managers were “amazed” to finally have this visibility—that they had never been able to work this way before.
I printed that email out. I still have it.
Because in that moment, I understood something that has guided every project I have worked on since: data engineering is not about databases, pipelines, or ETL processes. It is about people. It is about the patient who keeps using their CPAP device because a clinical coordinator called them at the right time. It is about the nurse who can finally see which patients need attention most urgently. It is about the family member who doesn’t receive the call that their loved one had a stroke.
That is what we build when we build well.
Learning to Work at Scale
From healthcare, I moved to what I still consider the most technically demanding project of my career: the Johnson & Johnson Enterprise Data Grid.
J&J is one of America’s largest healthcare companies. The Enterprise Data Grid is the data backbone of its global supply chain—processing data from over 65 ERP systems worldwide, handling more than 45 terabytes of data monthly, and tracking pharmaceutical products and medical devices across manufacturing, distribution, and delivery to hospitals and clinical settings across the United States.
I was part of a small, elite team selected specifically for the technical complexity of the work. I was one of the only women on that team, and also the only engineer managing the streaming of over 4,000 data tables across more than 20 critical projects simultaneously.
I will not pretend that was easy. The technical challenges were significant. The learning curve was steep. And navigating those environments as a woman—often the only woman in the room—added a layer of complexity that the technical problems alone did not prepare me for.
But I learned something important in those years: competence speaks louder than anything else. When the systems you build are reliable, when the data you deliver is trustworthy, when the monitoring dashboards you create become tools everyone depends on, the conversations about belonging tend to quiet down on their own.
I also learned that building data infrastructure at national scale—systems that track medical devices used in surgeries, ensure pharmaceutical supply chains remain intact, and give hospitals visibility into critical equipment—is not just a technical achievement. It is a genuine contribution to the people those systems serve.
Reinvention at NYU
In 2021, I made a decision that surprised some people: I went back to school.
I applied to the Master of Science in Computer Science program at New York University’s Tandon School of Engineering. I was accepted with an $8,000 merit scholarship.
People asked me why. I already had fourteen years of experience. A successful career. Why go back?
Because I knew the field was changing. Because I could see that artificial intelligence and machine learning would reshape data engineering in ways that required deeper theoretical foundations. Because I believed the best way to stay at the frontier of my field—and continue building things that matter—was to invest in my own learning, even when it was hard, even when it meant balancing graduate coursework with professional responsibilities, even when I was significantly older than many of my classmates.
NYU gave me more than a degree. It gave me a framework for thinking differently. It gave me a community of researchers and practitioners who challenged my thinking. And it gave me access to opportunities I would not otherwise have had.
One of those opportunities was an internship at Amazon Robotics, where I built data lineage infrastructure for an analytics platform supporting over 500 engineers and data scientists monthly. Another was joining Analytic Partners as a Lead Data Engineer, serving Fortune 500 companies across industries with AI-ready commercial analytics infrastructure.
What I Am Building Next
I have spent fourteen years building data infrastructure for other organizations. The question I am focused on now is: what comes next?
The answer, I believe, lies at the intersection of healthcare, education, and technology—and the communities I want to serve. I am focused on expanding my contributions beyond any single employer or project, applying my data engineering expertise to problems that matter most: improving patient outcomes, strengthening educational systems, and building AI-ready data infrastructure for institutions that increasingly depend on it.
I am also focused on giving back: mentoring the next generation of women in data engineering, writing and speaking in ways that make technical work accessible, and becoming the kind of visible, technically credible woman in this field that I needed—and often struggled to find—when I was starting out.
The work ahead is significant. The opportunity is real. And I am more energized about what is possible than I have been at any point in my career.
What I Would Tell My Younger Self
If I could go back to the young woman in Mumbai deciding whether to take the leap—to leave everything familiar and build a career in a country where nobody knew her name or her family—here is what I would tell her:
The unglamorous work is the important work. Data pipelines are not glamorous. ETL processes are not glamorous. Monitoring dashboards, CDC frameworks, and data lineage systems are not the things that make headlines. But they are the foundation on which everything else is built. Do that work with the same care and rigor you would bring to anything you wanted to be proud of. Because somewhere on the other side of that work, there is a patient who gets the call that keeps them healthy, a surgeon who receives the device that saves a life, a teacher who gets the insight that helps students learn.
Invest in yourself relentlessly. Go back to school when the field demands it. Get the certifications. Write. Speak. Even when you are nervous. Every investment compounds in ways you cannot predict.
Do work that matters. Not every project will change lives. But if you orient your career toward healthcare, education, supply chain, and public service, you will find that the motivation runs deeper than any title or paycheck ever could.
To Every Woman Reading This
You belong in this field.
Not because someone gave you permission, and not because the numbers are improving (though they are, slowly). But because the problems data engineering and AI are solving—in healthcare, education, supply chain, government, and beyond—are too important to leave to any single demographic or perspective.
Your experience as a woman—navigating complexity, managing competing demands, communicating across difference, and persisting in environments not designed for you—is not a liability in this field. It is a qualification.
The world needs more women building the infrastructure the future runs on.
I hope you will join me in building it.
