Day in the Life: Q&A with Senior Research Analyst Kayla Bollinger

How would you describe your role on the Analyst Team?

I recently joined the Analytic Systems functional group, a specialization within the Analyst Team. This means that the focus of my role is to help build scalable analytics by cultivating new tools and practices for our team to use. I also continue to work on analytic projects, often in collaboration with our Data Engineering and Research teams, to turn data into insights. Systems-related work comes up naturally as these projects evolve. 

Kayla alongside EA staff at the 2025 Ed-Fi Alliance Summit.

For example, this evolution could be a data source switching from flat files to a database. Learning the ins and outs of the database and training others to use it in their own projects would be considered systems work. Another example could be that a project has been growing steadily across many years, and the code structure that was initially built for that project can no longer adequately support it. In this case, the systems work would be to develop a new code structure that can support both this project and other similarly large projects across our team.

What interested you in working at EA? 

My academic background is in mathematics with a specialization in machine learning. Outside of academia, I knew I wanted to continue working with data, and I also knew I wanted to do work that made a positive impact on people’s lives. I began searching for nonprofit organizations that did analytics, and the ed tech space often showed up in this search. I got lucky and happened to be in the right time and place (living within walking distance) when I applied to work at EA.

We know that every day is different, but what would a typical day at EA look like for you? 

I’ll usually have a couple of meetings, but the rest of my time is spent coding. Most of my meetings are centered around a specific project and are split between “full team” and “analyst-specific” meetings. Often there are many different teams at EA collaborating on a given project (Analyst, Data Engineering, Research, etc.), so the full team meetings offer us time to connect. We usually discuss the project at a high level and make sure all teams are aligned on the general tasks that need to be done. Analyst specific meetings, on the other hand, are a place where all the analysts on the project can dive into the details and address any roadblocks that may have come up. When I am coding, it is typically in R or SQL. My coding work can range from data wrangling to implementing research methods to developing new internal tools for our team to use.

What skills do you possess that you find helpful in your role? 

I am a detail-oriented person. I take time to understand the details of a project, and I put care into presenting them and building code around them. Not only does this produce high-quality work, but it also ends up benefiting my other projects. The time and care I put into the details of one project help me to better understand and approach challenges in the next. So, even though sometimes focusing on details (especially the small ones) can feel slow, in the end I believe it accelerates my work.

What is the most rewarding aspect of your role? 

I get the most reward out of taking a complex or time-consuming process and transforming it into something simple and fast. Usually, this work is me fighting entropy—a buildup of inefficiencies or gaps of understanding that alone seem negligible but together hinder a process from scaling. I enjoy the slowness of it. It often requires many iterations, gradually whittling a process down to its essentials. By the end, I’ve hopefully landed on a process that is intuitive and fast, prime for another analyst to continue building on top of it.

What is your favorite project that you’ve worked on at EA? 

My favorite project is also the one that I’ve worked on the longest: South Carolina Growth. It's probably my favorite project because I’ve been working on it the longest. 

For example, South Carolina has adopted the use of Stadium, EA’s managed data warehousing service built on Ed-Fi. Because of this, we were able to transition to sourcing Growth data from Stadium, rather than from flat files. Not only has the use of Stadium significantly streamlined our data-sourcing work, but it also continues to open doors to other opportunities to expand our Growth work.

Stadium within EA's Ed-Fi-enabled product ecosystem.

If you had to choose a different team to work on at EA, which team would you pick and why? 

It would be a tough choice between the Data Engineering and Research team, but I think I would lean towards choosing the Data Engineering team. One reason is that I could still get to do the kind of work I enjoy most—building streamlined processes. Mostly though, I think my strengths lie more in enabling research rather than in doing the research itself. My work as an analyst, and even my research work as a graduate student, has shown me firsthand how pivotal an accessible source of large data can be. I think that being able to enable countless research projects by making data accessible would be really rewarding work.
 

What is something you enjoy in your free time? 

Some go-to fun activities for me are either reading sci-fi books or playing video games—mostly reading books when I need a detox from video games. I also like to try different hobbies, the most recent of which was pottery. These hobbies usually don’t last too long, so I think my real hobby is learning a little bit about a lot of different things. To keep active, I like to rock climb, but more recently I’ve gotten into CrossFit.

When you were a kid, what did you want to be when you grew up? 

As a young kid, I don’t think I ever thought about what I wanted to be when I grew up. As an older kid applying to college, I was forced to think about it but had no ideas. I remember asking my parents for suggestions of what I could be interested in. They suggested studying math, which I immediately scoffed at for being too boring. Joke’s on me, though, because I eventually ended up with a PhD in math. But my journey there was winding—I tried majoring in biochemistry, then film, then back to biochemistry, then sticking with physics for the rest of my undergraduate career, and then finally transitioning to math for graduate school. Looking back on my childhood this journey makes more sense—I always enjoyed all things science and movies.

What is something that you would tell your younger self about your career?

You’ll never have a clear picture in your head of what career you’ll want, and that’s OK. Just keep following the fun. When you’re having fun with something, you’re eager to work hard at it. Working hard almost always lands you in a good spot, so don’t worry so much about where you will land or how long it takes you to get there. And remember to take care of yourself—working hard is good, but a balanced life is better.