The decisions behind the journey.

Looking back, my internship search was fairly extensive: around 120 applications and 40+ interviews, which eventually led to several opportunities and offers across Canadian and U.S. companies.

I chose Data Engineering for two main reasons.

First, the team and manager mattered more to me than the job title.

During my interview at IMCO, I asked my manager what he expected from a new team member. His answer immediately stood out: he would not treat me as “just an intern.” I would work on real projects alongside full-time engineers, with similar expectations around ownership, responsibility, and delivery.

That was exactly what I was looking for.

I do not like using the word intern as a reason to lower my own expectations. If I only have a few months with a team, I want to learn as much as possible during that time.

“If today were my last day with access to this technology, how much could I learn from it?”

I had DS/MLE opportunities offering nearly 1.5 times the compensation of the role I chose, but some were structured around a single internship project. I chose the environment where I believed I would be challenged more, take on greater ownership, and ultimately grow faster.

Second, I wanted to understand the data foundation behind AI and machine learning.

Even the best model has limited value without reliable data pipelines, infrastructure, and production systems behind it. Computer Science and Data Science programs teach a great deal about algorithms, statistics, and modeling, but Data Engineering is an area that is often learned much more deeply through real production experience.

I saw it as an important gap in my background, and I wanted to fill it.

Compensation matters, but especially early in my career, I care even more about how quickly an environment allows me to learn and grow.

I get this question fairly often — including from professors at U of T.

I had several opportunities and offers to continue my graduate studies at Toronto, across programs in areas such as ECE, Statistics and Data Science, Mathematical Finance (MMF), and other quantitative disciplines.

But when I looked at my own background, I noticed an important gap.

I had built a strong academic foundation, but because much of my undergraduate experience took place during the pandemic, I had relatively limited exposure to real production environments and industry engineering.

I realized that another primarily academic experience was not necessarily what I needed most at that point.

That was one of the main reasons I chose the University of Waterloo MDSAI program. Waterloo has a strong connection to industry and a culture of work-integrated learning, and I believed that environment would give me a better opportunity to develop the practical experience that was missing from my background.

“What experience am I currently missing, and which path gives me the best opportunity to build it?”

I have thought about this seriously.

Several professors encouraged me to pursue research and stay in academia. I also received an offer for a second master’s program with a straightforward path to transitioning into a PhD.

Then I started working in industry.

Within my first month, I realized how much I enjoyed the pace of engineering. There was always something unfamiliar to investigate, a new system to understand, a business problem to solve, or a technology to turn into something useful.

That experience helped me understand an important distinction in how I currently want to learn.

A PhD is fundamentally about depth: spending years developing deep expertise and pushing forward a specific research problem.

Right now, I am more excited by breadth and rapid exploration.

I want to work across data systems, machine learning, AI engineering, infrastructure, and emerging technologies — and continuously turn what I learn into things that can actually be built and used.

That does not mean I have ruled out a PhD forever.

Perhaps after another decade in industry, I will discover a problem that I care deeply enough about to spend several years studying. If that happens, I would be very open to returning to academia.

For now, I want to explore broadly, build in the real world, and eventually discover which problem is worth going deep on.