Day in the Life: Q&A with Senior Data Engineer Angelica Lastra

How would you describe your role on the Data Engineering team?  

Data Engineering requires a specific kind of thinking. It’s not linear or circular, but rather: forward and backward and big and small all at once. I am always asking, “What will break this? What hasn’t been imagined yet? What needs to be true six months from now for today’s decision to still make sense? What has already been done that I have to adapt to? How can this solve immediate and future use cases?” 

At its core, my role is connection and trust. I take raw, messy, often inconsistent data, and I clean it, transform it, and route it. I make it trustworthy. And like anything worth trusting, that requires attention. A moment of inattention in a data pipeline isn’t always loud. Things don’t always crash dramatically. Sometimes things quietly pass through when they shouldn’t. Sometimes something slowly breaks and it’s hard to notice unless you are really looking. 

So I spend a lot of time paying attention to the details, to the shape of the whole, and to what my downstream colleagues (analysts, researchers, people who may never write a line of code) will actually need from what I build. When it comes together, when the architecture is clean and the logic is sound and the pipeline can grow, contract, and connect to new things without breaking, I feel quietly satisfied. 

My role is a small thing in the grand scheme. Realistically, I’m just sitting at a desk with a couple of monitors, a mouse, and a keyboard. But somewhere downstream, this work reaches students and educators across the country. And there’s something so rewarding about knowing that whatever I made at least did not add to the chaos, and in fact, made the work of those who need it a little bit easier. 

What interested you in working at EA?  

The path that led me to EA was not linear. Before arriving here, I worked as a designer in an architecture firm, and for a long time, I believed that was my ultimate dream. But my career journey shifted, unexpectedly, when I got my very first job at a place called the Community Design Resource Center, a place where architecture, planning, art, activism, and community development all folded into one another in service of a single conviction: that design is a strategy for change. 

I had the honor of working alongside community members across Houston neighborhoods, not on their behalf but with them, helping them translate what they knew about their own neighborhoods into something legible to the city, to funders, and to programs that had long overlooked them. We did not arrive with answers, but rather we sat with people, listened, and translated. Behind the scenes, all the community data, surveys, resident input, and neighborhood plans were held together in duct-taped Excel spreadsheets and hard drives—the earliest version of data work I had ever done. 

I left that work understanding two things about myself that I have not been able to shake since: 

• I need to feel that what I do has an impact, not abstractly, but on real people, and I need to feel that the people around me feel the same way. I want to be proud of the work I do. 
• Data drives informed decisions, it is tangible proof of the struggle and challenges some people feel in their day-to-day, and just like design is a strategy for change, data is a strategy for action. 

When I got into more data-focused tasks, I wanted to work somewhere that had that same feeling. EA was the first place that truly spoke that same language. In my eyes, EA echoes a sentiment that data is a right and that everyone should have access to it regardless of skills or tools. We work with schools, students, and the tech community to ensure that the right information reaches the people who need it in order to drive the future of students across the country. We believe in collaboration, in sharing our code, and in teaching others how to enable analytics themselves in their own data. I love how human-centered, caring, and joyful my teammates are, how casual everyone is and yet so professional, how smart, and how diverse. I genuinely feel seen as a human being on my team, and I see that same care in the way EA treats our partners and our mission. The impact we have on students and schools across the country feels tangible and I feel lucky to be a part of it. 

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

My day starts the same way every time, with a walk to the nearest coffee shop for a small cup of coffee. It is usually quiet in my neighborhood in the mornings and I always try to make a point of appreciating that. It gets my mind ready before the work begins. 

Then I sit down in my office, it’s small with no windows, and yet it never feels dull or gloomy, because Echo is there. Echo, my chowbrador (or is it labchow?), has a way of making any room feel lively.

I check my meetings for the day when I first sit down. They are mostly internal, focused on checking in or planning ahead, and they move fast. One hour I am talking through work for Texas, the next I am looking at data for Indiana, and the next I am somewhere else entirely. The projects at EA are always multiple, always in motion, and always at different stages. There is a rhythm to it once you learn to manage it and I tend to find myself holding many things at once without dropping any of them. 

Since moving into my role as a Senior Data Engineer, a good part of my day has shifted toward the upstream work, which involves planning for the quarter, unblocking workstreams, architecting solutions, and advocating for my team and the projects we work on. It is less about writing every line of code and more about making sure the people who are writing it have the right answers and guidance. I still get a lot of time for active coding though, and honestly, that time still feels sacred to me as it is my favorite part of the day. 

By the end of the day, I pick up my empty coffee cup that has been sitting on my desk for way too long (at that point, it really mirrors me), schedule any messages that are too late to send to folks in other time zones, log off, and look forward to tomorrow’s new cup of coffee. 

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

Precision is critical in my role. The ability to hold a small detail in focus while also keeping the larger system in view is something I rely on every day. The tools I work with—SQL, Python, dbt, Jinja, Snowflake, Airflow—each has their own logic, their own way of failing or behaving unexpectedly. Learning to trace a broken pipeline back to its source, without becoming too overwhelmed, is a skill that took time to develop. Ambiguity is normal in this work, and learning how to resist the urge to solve something too fast has served me and the people I work with more than once. I have also found that the work is more relational than I expected. Partners come to us with problems they can barely name yet. In those moments, technical fluency only gets you so far. Listening, asking the right questions, and translating what someone is seeing into something we can act on—all of that matters just as much. 

What is the most rewarding aspect of your role?  

There are many rewarding things in this role, but the ones I feel most often are smaller moments. I feel so much joy when I fix broken code or when I untangle a pipeline that has grown complicated over time. Cleaning, transforming, and fixing. Although it sounds tedious, when it’s done it makes me feel so light. And in almost every one of those moments, I learn something I didn’t know before. That combination, solving a problem and learning something new in the process, is what makes me aware of my growth and impact every day. I find it extremely rewarding.

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

This is genuinely hard to answer. I have worked across so many projects and I find something to love in most of them. 

If I had to name one, it is the pipeline I built alongside my teammate, Sam LeBlanc, that automates user management for our dashboarding product. What started as a “simple” Airflow DAG (a series of Python scripts that get run on a schedule), one that pulled new users, sent them invites with the right roles, and checked every hour for anyone whose access had changed or lapsed, turned into something that has quietly shaped who I am as an engineer. 

It was one of my first times working deeply outside of dbt, living almost entirely in Python. Working alongside Sam on that project taught me so many things that I still carry with me in how I write Python code today. The language clicked for me in a new way and I have not looked back since. 

What makes it my favorite, though, is what it became. Since we wrote it last year, I have been working with my teammate, Jay Kaiser, and colleagues across Cloud Engineering and Software Engineering to expand its behavior and open its results to tools that never had visibility into it before. I couldn’t have imagined that one DAG could become a thread that connects me to other parts of EA that I do not usually get to collaborate with, and it has opened doors I did not know were there. I have genuinely loved every part of it. 

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

Cloud Engineering! I am so curious about understanding the things I rely on but cannot fully see. Every day, I interface with servers and infrastructure that hum along quietly beneath my work, and I always find myself curious about what lives on the other side of that boundary. Their team is so pivotal to what I do, and yet their world feels a little mysterious to me. I want to understand the tools, decisions, and architecture that makes the work I do possible. 

What changes do you anticipate in your field in the next year?  

It’s no secret that AI has already had a large impact on the profession. More and more companies are integrating it into their workflows and services and I think that trend will only continue. The hard part is predicting exactly how because the technology itself is changing so fast. I can’t say with confidence what Data Engineering will look like in five or ten years, but I have been hearing a lot about the role of Data Engineering in harnessing agents and context for those agents. I can see a new strand of Data Engineering with an AI focus, where our role becomes less about moving data for people and more about moving data for agents.

What is something you enjoy in your free time?  

When the weather is nice, I really enjoy hiking. Being outside with no distractions or worries makes me feel serene. It’s like when you are really stressed and you take a moment to breathe, and in that brief moment, the stress leaves with that breath. That’s how I feel when I’m outside in nature.

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

I wanted to do so many things! My first memory is wanting to be an animator. I loved to draw from a very young age and I dreamed of making my own short films or even writing children’s books like the ones I grew up reading. I am still deeply fascinated by them and the way they can convey such meaningful messages in an approachable way. I seek them out everywhere I go and at this point, my collection speaks for itself.

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

There are two things I would want to tell my younger self about my career: 

(1) We tend to believe that the path we have chosen and given ourselves to is unmovable, that the depth of our commitment is itself confirmation that turning away would mean losing everything we put in. I spent years in architecture, which I love, and which shaped how I think about systems, structure, and design in ways I still draw on today. But the moment I understood the path was not right arrived quietly. I had interesting work, great people around me, and the right skills for it. By most measures, it was working. And yet when I asked myself honestly whether I was fulfilled, the answer was no. That was enough. 

Leaving was not easy. There is a particular fear in stepping away from something you have built your identity around and a particular weight in admitting that who you believed you were going to become is not, in fact, who you are. But I have come to think that knowing when to stop is its own form of clarity and a willingness to revise your conclusions when the evidence changes is a strength, not a weakness. 

What I found in data engineering was what I had been searching for all along—the conviction that the work lands somewhere real, impacting students and schools and the people who show up for them every day. That same pull that drew me to architecture in the first place. So I would tell my younger self: The path that costs you everything is not, for that reason, the right one. Sometimes the bravest thing a person can do is set down the dream that was never quite right and go find the one that is. 

(2) I would also say something about imposter syndrome because I have struggled with it often. It tends to be worse precisely when it is the least warranted. The more you learn and the more you are in spaces where that learning is accelerated, the more clearly you can see the edges of what you do not yet know. Competence and opportunities, paradoxically, can make you feel less certain. I have come to think of that as something to be grateful for. Being surrounded by people who are exceptional at what they do is not a reminder of what you lack; it is evidence that you are exactly where the learning happens. 

What I have found useful is this: when I look back at moments where I doubted whether I belonged, I can see now how much I have grown since then. The doubt I carried then would not survive who I am today. That is not reassurance; that is data. It tells me that the syndrome was never a verdict. It was just a snapshot of where I was in the process. 

So I would tell my younger self: trust the process of accumulation. Keep growing, keep gathering experience, and give yourself enough time to look back. The question worth asking is not whether you belong, but whether you are learning. If the answer is yes, you are already further along than you think.