From Finance to Data Engineering: Hanna Lee's Success Story

It’s always a great moment when we hear from users who’ve landed jobs with the help of the tools we provide at Interview Query. We believe these firsthand accounts can inspire others, give tips on interview preparation, and highlight what working in these roles is like.

This week, we caught up with Hanna Lee, a recent USF graduate who started in finance before deciding to become a data practitioner. We discussed her journey after making this decision and how she leveraged this platform to land a role as a data engineer at a clean energy company.

My initial educational background was in finance. I worked in that industry for 5 years using electronic trading platforms for forex trading. Back then, I would trade using algorithms others wrote, but after around 4 years, I decided I wanted to write my own algorithms. That’s when I decided to pivot to data science.

I figured a master’s in data science would be a great place to start, and I chose the program at USF. At the time, it was the only data science program I found that offered a practicum alongside regular studies, so I was able to intern while also taking the courses.

For me, the practicum was an important consideration because I knew the importance of actual work experience. I actually started in investment banking after taking a gap year and a co-op with HSBC, so I knew that many of the skills needed for work, such as stakeholder and project management, can only be learned on the job in an actual work environment. So, practical experience is important to me, and I believe a hiring manager would think the same.

The master’s course at USF was quite compact because it’s a 1-year program. A normal program would have like 3 to 4-month semesters, but this one was condensed into only 2 semesters, and we were taking around 4 units each semester. This made the program quite intense.

The program gave us a good overview of everything as far as data practitioner fields go so different students could focus on different areas like data engineering, business intelligence, data analytics, or data science.

The students taking the course were close, and this really helped during job hunting. We formed groups and conducted mock interviews with each other. We also exchanged the interview questions we’d been asked at different companies. I liked this because I’m a team player, and I like facing challenges as a team. I also think great friendships are formed by overcoming hurdles together.

I got access to the platform when Interview Query partnered with USF. I think before Interview Query, people mainly used Leetcode. But Leetcode only focuses on the technical aspect with a robotic question, and you get to code your answer in.

With Interview Query, the questions are presented like an interviewer would ask. Other people can also give answers at the bottom, which offers additional thoughts and ideas on how to answer the question, making it more of a discussion. There were also plenty of questions with good answers, and I could mark these and revisit them.

During my job search, I was aiming for a growth data science position, but the tough job market in 2023 meant that most available positions were in AI or data engineering. Most of my final on-site interviews were for these roles. Luckily, Interview Query covers all these positions and helped me to easily focus my preparation for specific positions. I also liked the company focus of the questions.

I got a data engineering job for a marine clean energy company. I also had 2 other final interviews, one with Meta and another with an AI startup. Some recruiters from companies I interviewed at but didn’t get offers from have also recently reached out again to ask if I’d be interested in interviewing with them again.

My previous job experience was definitely an advantage compared to the fresh graduates, and I also had access to a forum for Chinese students in the US called 1Point3Acres, which was a great help, although the content is in Mandarin.

It would be great, and I’d also be happy to provide feedback because Interview Query is a good product. I hope more people can benefit from using it.

I tried the mock interview feature back then, but I’m looking forward to trying out the new AI Interviewer this time around. Interview Query is also one of the few platforms where I can refresh my knowledge of calculus and practical statistics.

Because of my business background, I felt like I had a lot of product ideas, so I was focused on data science initially, but there aren’t many growth data science positions at the moment. We do have a lot of data because of LLMs and building pipelines, and getting good data with good integrity has become really important. That’s where roles like data engineering, data governance, and AI engineering come in.

Right now, our team is kind of lean, so it can get really busy in my role as a data engineer, but this has also given me a chance to own projects end-to-end. I might be running as many as 3 projects at the same time, which is a bit crazy, but it also allows me to learn really fast. With ChatGPT, I can also get answers quickly if I don’t know something. So right now, it’s really about how fast you can learn. I’m also very open to working on new things as long as I have good teammates.

Usually, data engineers take 2 or 3 years before they can own a project, but I can already handle the entire data lifecycle management.

My job is very focused on climate tech, and such products are popular right now, but I also hope to be seen as more than a clean tech person in the future.

I think so. The questions posted on Interview Query show the thought process and come with a model answer that is worded more naturally. I think Leetcode still matters, but I had 30 interviews in 2023, and only five had Leetcode-type questions. Companies have their own question banks, and you don’t tackle the complicated Leetcode-type problems on the job. I’d describe it as the difference between book smart and street smart.

This is especially useful for international students whose mother tongue is not English, people pivoting into data roles, and fresh graduates. These model answers give them something that sounds closer to what a normal person would say instead of something that makes them sound like they’re reciting a textbook. I have also watched Jay’s YouTube videos before. So, amusingly, I always kinda read the answers in his voice.

I also think it could be really useful to international students because of the 90-day time limit for finding jobs. ChatGPT is another useful tool at this stage, but unlike with Interview Query, I can’t be sure about the accuracy of the responses.

Hanna Lee’s story is a great example of how people are shifting from other industries to become data practitioners and using their experience to jump-start their new careers. She also shows how you can leverage different platforms during your job search and how Interview Query offers a unique experience, helping candidates beyond providing textbook answers to interview questions.

If you’re considering a career in data, we offer many resources to help you in your journey, including company interview guides and interview questions. We also give users access to coaching from industry experts so they’ll know what to work on before their next interview.

Finally, you can also visit our job board to check out open data practitioner roles. We look forward to telling your success story in the future!