In high school, the pressure is put on students to figure out what is next. What do you want to do with the rest of your life? Students are asked to decide between working, pursuing a degree, going into a trade, or other. If you go to college, a similar question is asked: what do you want to major in to do with the rest of your life? And by the time you are looking for a job, employers ask: where do you see yourself in 5 years? What do you want to be doing with the rest of your life? The hard truth about figuring out what you want to be is that it may take many years and many tries before you get it right, and that is okay.
When I started high school, I attended a technical school in which I could take electronics technology. The idea of building circuits and playing with robots everyday interested me, but not enough. As college applications rolled around, I applied to many different schools and ultimately decided to become an English major. I wanted to explore my writing interests and get away from the hardware I had spent four years dabbling in. The next two years were wrought with indecision, concern, and denial.
I do enjoy writing, especially technical writing and documentation. But I could not see a career in it that would make me happy. After two years of taking different science, math, English, and art courses, I signed away paperwork that would remove me from that school. Instead, I transferred to another college to pursue electrical engineering and get back into STEM.
It is funny, looking back on it now because I was only an electrical engineering major for a month or so. Remembering my electrical technology days in high school, I knew I was more interested in programming than in hardware design. It was then that I switched from electrical to computer engineering. This switch gave me the best of both worlds, getting a mixture of classes in both hardware and software. As I took those courses, I began interning, exploring both software and embedded engineering fields. I enjoyed this mix and was interested in becoming an embedded engineer upon graduation. As I looked into courses that would help me achieve this goal, I began to develop my senior project and apply for master’s programs in electrical engineering.
I focused my senior project on embedded programming and interfacing the sensors to an application that would collect and analyze the data. And just as I began to feel secure in my decision to become an embedded engineer, I began to question my decision. Upon completing my project and graduation, I strayed away from embedded engineering and an electrical engineering master’s.
Instead, I accepted a job as a DevOps engineer working on servers and studying part-time for my Data Science masters. While working and studying part-time, I made one of the hardest decisions I needed to make. I quit my job and went back to school to pursue my master’s full time in data science. It was there that I learned what it meant to look at data, incorporate it into different projects, and be able to speak to it. It may have taken me a few extra years, but the hard truth about figuring out what you want to be is that it is not an easy path.
After receiving my master’s, I took a job as a data engineer working to develop an application that would allow users to search up metadata about all available datasets the company had. As that job came to an end, I was hired as a senior engineer in applied data science.
As I sit back and look at my journey, each step to become a senior engineer in applied data science taught me different skills that I use today in my everyday work:
- Written and Verbal Communication (English Major)
- Interacting with Sensors on Hardware and Collecting Data (Electronics Technology / Computer Engineering)
- Software Development (Computer Engineering / Data Engineering)
- Servers and Diagnosing Server Issues (Dev Ops Engineering)
- Continuous Integration and Deployment (Dev Ops Engineering)
- Developing Robust Data Sets (Data Engineering and Data Science)
- Analyzing and Visualizing Data (Data Science)
- Working on Big Data and Big Data Applications (Data Science)
These skills and many more have come in handy as I navigate my current role and lead my team.
If I have learned anything from this experience, your path may not be clear when you start. As I traversed my path, I changed my mind more often than not, and I am glad I did. If I had waited, I worry that I would be stuck in a career I was unhappy with, performing my work to stay afloat instead of trying to make a difference in my field. Finding what made me happy to go to work every day was an eye-opener. Being in a job that brings me joy and allows me to make a difference has shown me that I did make the right choices in traversing my path. It may have taken me a while, but I found it, and I love it.
Final Thoughts
As you traverse your journey, know that the most challenging truth of figuring out what is right for you is knowing that it may not be an easy path.
- The path is not always linear. Be open to changing your mind and finding out what is right for you. It is okay not to have it all figured out right away. It took me a while to accept that I had not figured it out as fast as others. Realizing I wasn’t alone in my journey helped show me that it was okay.
- Enjoy the journey as you are going through it, and know that you will find what you love to do. Key lessons and skills learned will come from each step of the way. As I shared above, there is always something you can take away, even from a bad experience. Take those experiences with you as you go onto the next thing and grow from them.
- Know when to say no. If something is not working out for you and you are not happy with what you are doing, see when you can step away from it and say no. Walking away from DevOps showed me that I could say no when I felt the opportunity wasn’t right for me. It is okay to say no and find an opportunity that suits you better.
How did you find what you enjoy doing? What challenges did you have to overcome to get there?
If you would like to read more, check out some of my other articles below!
Why Does Reliability and Stability Matter in Data Science?
One Big Lesson on Creating a Software Library
Remote Work Can Make it Hard to Stand Out as a Strong Data Scientist