In 2017, I started my first job as an Engineer Intern in the Predictive Analytics team, and after a year, I was promoted to Data Engineer. But to be honest, I was never sure if the titles I got can really describe my role in ChannelAdvisor since I was the first Data Engineer in our company. There were no clearly defined responsibilities in this role at the beginning and the responsibilities changes based on the tasks I did. Hence, a lot of the time, I just see myself as a problem solver who does whatever she can to find out solutions to productize analytics products and then make her team proud. I think it is actually not bad to not strictly defined who you are, but from the career plan perspective, I have to admit that a lot of time I feel quite lost.

Until today, after reading this blog post and watching its video, I suddenly realize that “OMG! It turns out that I am a Recovering Data Scientist and my past experience told me that I enjoy being a Data Engineer!". Now, I feel so relieved since I finally see a concrete career goal and the roadmap to achieve it is getting more and more clear. 😊

According to the post, here are why I believe Data Engineer will be a good fit for me.

You Like Creating Automated Systems

I created an MLOps system for my team. I found that the sense of achievement I got from successfully connecting all the open source tools together and then seeing all the data flow run in the system automatically and smoothly is much more than fitting a machine learning model with good accuracy or providing a good data analysis presentation to stakeholders.

You Like Working With Cutting-Edge Tech

Since 2020, every year I attend multiple tech conferences, learn how other people use new tech to solve their own problems gracefully, and then bring the ideas back to my work with hands-on POC projects. For my own personal side projects, I love to use the alternative tools of the ones I use from my work since I like trying out different tools, doing comparisons, and then getting better ideas of the best use cases for each tool.

You Like Data

Of course I love data. Or, I would not choose Statistics as my major. It is just before this, I always thought the only way to really make data shine is going through machine learning modeling or some ad-hoc analysis. But from my past five and half years of experience, the thing I learned the hard way is that a data product will not succeed unless you have good quality data and a solid system to support the whole service. Once you meet these two requirements, a basic machine learning model can make your product shine like gold.

The blog post: Is Data Engineering For You? Maybe you’re a recovering data scientist?
The video: