top of page

How I Transitioned into Data Engineering from Finance

Updated: Jan 8

In early 2019, I was working at Ernst & Young as a Senior Finance Advisor helping teams with various financial projects but I was so unhappy and knew that I needed a change. My older sister moved to California and I was eager to move out there with her so that she would have some support system. This is when I started to get serious about applying for jobs in California; no matter what the city, I just wanted to make the jump. I sold my townhome and rented out a room via Airbnb in Atlanta.

Focused data engineer immersed in coding, building the foundation for innovative solutions. Harnessing the power of data for a smarter, connected future.

Just about every interview that I participated in needed coding skills in some capacity. I would get through to the final rounds of these interviews and for some reason, I could not get over the hump of getting an offer.


COVID started to rise and like all of us, I had to remain in place. During this time, I was researching my next career move. I knew that I had to become more tech-savvy from the interviews that I conducted, so I was thinking about the best way to transition into tech with the skills I had already acquired over the past 10-plus years in finance.


Data Science it was! It was the best way, in my opinion, to transition into tech while still leveraging the skills that I acquired in finance. So what did I do? I started to learn how to code in SQL and Python. Python first. I would code every single day from February 2020 until August 2020. Slowed down my coding practice because I got a job as an Oracle Database Administrator and I wanted to focus most of my time learning the role.


As time went by, I got a little distracted by life occurrences and the demands of work. Maybe I should say alot distracted. However, I got my wake-up call in November of 2022, when OpenAI's ChatGPT was released. I was so amazed by this chatbot; I spent hours giving it prompts and studying how large language models work.


At work, I connected with the Data Engineering Manager and discussed with her my interest in becoming a data scientist and she gave me a book that was based on ARIMA modeling. I also asked her what was a data analytics process or model that was needed in the company that we were possibly going to outsource to a third-party company. She told me we did not have a predictive sales model yet within the company.


So what did I do? I started to figure out how I could create this predictive sales model. The next few weeknights and weekends were dedicated to finding the right model for this project. It was great practice, as I built models using Facebook's Prophet model, ARIMA models, Linear Regression models, and XGBoost models. There were a few nights of frustration and maybe even a little doubt but through persistence, I figured out the right model and features that made my predictive sales model 99.5% accurate. What a beautiful reward!


I presented this model to my supervisor and the Data Engineer Manager; they seemed impressed and my confidence was on 1,000. After presenting the model to my supervisor, I voiced my interest in transitioning onto the Data Engineering team. Because I was already excelling in my current role as an Oracle DBA, the ask was easy and my supervisor started to connect the dots to start my transfer onto the Data Engineering team.


Here I am now, getting more in-depth experience with Azure Data Factory, Power BI, and SQL Server Management System. I am happy.


The key is to continue to learn. I am still practicing SQL coding most days of the week and taking coding courses to produce at a high level.


Check out this cool article where ShoutOut LA interviewed me.

Comments


bottom of page