Specializing in distributed systems, backend, and data-intensive computing. Computer Science graduate (May 2026) from the University at Buffalo, with a Certificate in Data Intensive Computing. My coursework and projects center on distributed systems and large-scale data processing, including consensus protocols, peer-to-peer routing, fault detection, and the Spark/Hadoop pipelines that move data through them. I also practice data structures and algorithms regularly on LeetCode, using NeetCode's roadmap to guide my study.
I completed an internship at a software company located in Quito, Ecuador, from June 2025 to September 2025. Ecuador is a country that is located in South America, and I was there during my summer break. At that time, I was looking for an internship so I could gain more experience, although I did not receive a response from any companies in the United States. I decided to contact a company located in Ecuador through someone I knew to see if they could offer me an internship opportunity while I was there, and I was happy that they offered me an opportunity. When I arrived, I began to learn with them. This company is a Software house with more than 8 years of experience in research, design, and development of technological solutions. They have many products that they have launched, including a Document management and workflow automation system, an insurance broker software, accounting software, invoicing software, and custom-made software for specific requirements.
During my internship, I worked with the Invoicing team, who were in charge of the creation and maintenance of a product called EasyFact, which was designed to create invoices and manage receipts. I was able to attend many daily meetings the team had, which taught me how they assigned tasks within a project, how they communicated concerns and suggestions, and how progress was tracked and reported. The main issue the team had was to update and generate a more scalable system to generate receipts.
The tools I used were Visual Studio, .NET, SQL database, EF Core migrations, and REST APIs.
First, I learned about SQL and databases. The other interns and I were tasked to get familiar with the technology, so I created a database and got familiar with SQL language to insert rows, retrieve tuples, and perform basic operations. Through this first part, I learned a lot about the ideas of Databases, why they exist, and how they are different and sometimes better than an Excel sheet. SQL Syntax was something I had never experienced before, and surprisingly very easy to do. By doing this, my understanding of databases, how data is stored, queried, and managed, has increased greatly, and I am more knowledgeable now than I was before. In many of the data-related classes I am currently taking, I recognize many of the topics we are speaking about, and if something is completely new, I can relate it to stuff I have seen before.
The team had a playground SQL server dedicated to teaching interns and running tests without harming any of their main databases, where we were able to mess up without any worry. Here, we practiced larger and more complex queries; they did not check our work in any way. The tasks were treated like a self-directed learning course, where we were expected to take responsibility for our own progress and to learn independently. Here, they also showed us examples of SQL operations during a short live session via Zoom.
At first, when we were tasked to simply get familiar with the new technology, they suggested we use DBeaver and SQL Server, as those were the tools they used for their applications. I created a database that ran locally and used Google Dataset Search to grab something I could play around with. I remember using a dataset of Ecuadorian housing and property market details, which contained tens of thousands of rows of information. It had things such as price, location, details about the properties, and much more. Creating a database, populating it, and performing my first queries was a fundamental skill and concept to grasp, and it later helped me understand the general purpose and structure of a Database. This was done for around one week, from June 11 to June 23, 2025, approximately. Then we jumped to performing a list of queries they assigned to us using their data, which was on a private server separate from the main server, so when testing new features and learning, nobody could hurt anything. This data came from real anonymised users and was served by the developers. They had millions of records from which we would learn more complex queries and understand more in-depth how their data was processed later on. This private server was structured the same as their deployed versions, just had fewer records, and was fully functional.
During the internship, I began with a public dataset of Ecuadorian house/property prices, which contained tens of thousands of records. I executed various data-cleaning techniques using the Pandas library on Python, such as counting records, finding out how many records had empty values, handling those empty values, checking for incorrect or inconsistent formatting of the data, looking for duplicates, and understanding the data overall. Some simple SQL queries were also performed with this data set after I loaded it into a local SQL database. Then we moved to an SQL Playground server that they provided us. This server contained many tables and hundreds of thousands of records, where we practiced more complex queries. This data was already cleaned and correct; no data cleaning was needed.
The main methods we used during this portion of the internship were SELECT, INSERT, UPDATE, and DELETE, along with more complex queries containing JOIN, SUBQUERIES, WHERE statements, and more. The goal of this portion of the internship was to understand how large datasets are queried, processed, and ultimately served.
Steps taken to represent the methodology used:
No machine learning methods were used since it was not the focus of the company. We mainly focused on how to build a RESTful API server and how Databases work.
To speak more about the data, this data was generated from real users who have used the app, where it served as a copy of real records with randomized identifiers, such as fake names and more. It reflected real-life data and behavior, which I believe is way better than randomly made-up values. To describe the quality of this data, I would say it is realistic, which was perfect for practicing, testing new features, and more. About the data itself, we never looked or dived into data cleaning, so I would not be able to tell if this data was perfect or had any missing or redundant values, but for learning query language it worked very well. Realistic data is excellent for this internship because it was the same data and queries that developers used during the production of their software, showing real business behavior and real records. Anonymization also provides very important user privacy, which makes for good business practice.
The goal of data analysis in this internship was to understand large amounts of information, how they are structured, how to retrieve relevant information from large datasets through queries, and to understand how a company processes and organizes its data. It also helped me see how companies have dedicated playgrounds and testing servers where they can develop and test new software before deploying it. It was not just queries I learned, but also companies' and developer teams' processes to develop, test, and deploy systems.
The team also introduced us to the REST APIs and explained how they work during many short Zoom meetings. I learned the purpose of APIs and how the applications and backend communicate with each other. During the various meetings, we were introduced to different methods for GET, POST, PUT, and DELETE, what each one means and does. I also learned how the backend received the various requests through HTTP, and how it interacted with the database to deliver that. We were tasked to create endpoints to retrieve data from the playground database, so I remember creating methods for INSERTING tuples, DELETING tuples, GETTING tuples, and UPDATING tuples. From this, I learned about the flow of how data is moved between parts of a system, how responses are assembled, and how they are received. They also helped me understand that data management is not just about storing information in a database but also sending, receiving, and updating that data in a certain way.
The team also taught us how their application was organized; they used a .NET environment. During many Zoom video calls, they explained this by showing us their actual application code and explaining how different layers of their system connected. At that time, I had already learned C#, so it was not very complicated to follow the code they were reviewing, which helped me understand how a company's software is structured. They continued to explain to us what challenges their team was facing. The system they had could not handle an extremely large volume of transactions from a major insurance client in Ecuador, which had slowed down the system, and sometimes required upwards of two days to process all their transactions between them and a government agency. They were building a bigger and better system from scratch, where they had scalability in mind. They explained that they would build a system using a parallel approach; they mentioned that the problem was not storing data into a database, but rather processing it all before storing. They presented a plan to create a parallel system that used threads, tasks, and asynchronous processing to better the runtime and allow scalability. In addition to that, they mentioned that they would also upgrade the hardware to allow for more processing power and efficiency.
Through this internship, I learned a lot about data-related systems and how companies have professional environments. Before this experience, most of the concepts I knew were only what I was taught in a classroom, and to see how it all comes together in a professional setting is a huge experience for me. I was able to learn about databases and data itself; it is such a relevant experience for my future. By learning about SQL, databases, REST APIs, and .NET environment, I have a great understanding of how all comes together to build a complete system.
This experience also helped me in later classes here at UB. Where many of those concepts were repeated or related, and I instantly recognized if I was learning something I had seen before, the understanding of how data is processed was also implanted into my mind. Before starting that internship, I had a good foundation in distributed systems, where scalability was key, but I had never thought of relating scalability and processing with data and data handling. To see it all come together in a real implementation that is used by real people helped me understand that backend structure, data management, and processing efficiency can be closely related.
By attending the team's daily meetings, I was able to observe how tasks were assigned and how progress was tracked, how communication was handled, and how they got together to come up with a solution to a given problem. I learned that developing software is not only about writing code, but also about collaborating with a team, since most likely you will never be alone on a project.