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The 4 Commonly Asked Questions Answered
And ask your questions too in the comments!
👋 Hi, this is Sarah with the weekly issue of the Dutch Engineer Newsletter. In this newsletter, I cover data and machine learning concepts, code best practices, and career advice that will help you accelerate your career.
Over the past two weeks, I have given presentations on LinkedIn and resumes to the attendees of’s boot camp. The group is diverse from just starting out their careers to over ten years of experience within the field, so I receive a wide range of questions.
Here, I would like to answer some of the most commonly asked questions, not just from the boot camp but also as I have grown on LinkedIn.
I will also be answering your questions in the comments as well.
Thanks to Delta for sponsoring this newsletter! I am a huge fan of Delta Lake and use it every day both in Data Engineering and Machine Learning.
Q: I cannot get to conferences because they are too far away from where I live. What are some other ways in which I can network?
Let me start by explaining how I feel when I get to a conference. When I am at a conference, you will see me in two places: somewhere at the edge or outside of the building. It is not that I am a fan of people — I love getting to know people and building relationships. I am not a huge fan of large groups of people confined in a room.
I used to beat myself up about my networking skills, but I've recently discovered that there are more ways to network than just at conferences. The biggest change I made was to start writing on LinkedIn and improving my technical writing, which has helped me connect with many people in my field.
If you are struggling to come up with ideas, I suggest reflecting on what you wish you had known two years ago and writing about those topics.
Q: What are the technical skills you would highly recommend data engineers learn?
This answer applies to machine learning engineers as well.
Spark and Docker. Regardless of what side of the data engineer archetype you want to be, knowing Spark and Docker will literally open up doors for you. I have seen it happen with the number of positions I qualified for. Why?
Data engineers use Spark to build out and process an enormous amount of data (both batch and streaming) and most companies use Spark in some way whether that is managed by one of the cloud vendors or Databricks or a self-managed version usually build on Kubernetes.
Data engineers use Docker to containerize their applications. This enables them to package their code and dependencies in a portable way, ensuring that their applications run consistently across different environments, regardless of the underlying infrastructure.
Both of these tools address a common problem that many companies are facing today, which is why data engineers with experience in Docker and Spark are in such high demand.
Q: What’s your biggest struggle as a woman in the tech field?
I am not sure if there is one specific struggle that I frequently face. However, there are a few challenges that consistently arise in my career as a data or ML engineer.
One of the most pervasive is the lack of female representation in these industries. As a woman in these fields, I have often found myself to be the only female engineer in a given team or even in a meeting. This not only makes it more difficult to foster diversity and inclusivity of thought, but it can also be a lonely experience. One reason why I write is to provide a female face that readers can relate to. Through my writing, I have connected with other women who have made the tech world a comforting place to me.
Another hurdle that I frequently encounter is deciphering the intentions of others when they speak to me. Often, I am given explanations for concepts I already know for reasons that are not obvious. Sometimes, this is done with good intentions as the other person wants to ensure that they have a good understanding of the material. However, there have been times when this is not the case (usually tone shows the difference between the two). This is a thought process that comes up more often than I would like, and granted, it gets rather exhausting at times. Plus, how can I respond to this type of conversation in a way that ultimately contributes to the growth of the project?
Q: How much do certificates matter?
Certificates can be incredibly important in your career, particularly when you are seeking a new job with a different skill set. They can help you stand out from other candidates and give you the confidence to go for that dream job you've been eyeing. Let’s take a look at how certificates helped me in my career.
When I graduated from my master's program in theoretical chemistry, I had some experience with data science projects, but most of them were in chemistry and felt too abstract to apply to the field of data science. I was struggling to find a way to break into the data field until I discovered Tableau. Unfortunately, I was not able to showcase the projects I had worked on in my tutoring position, so I ended up getting a certificate to still showcase that I could use Tableau.
My first two positions in data were at consultancies. Consultancies often require their consultants to take certificate exams to demonstrate their skills and show that they are up to date with the latest industry standards. The first certificate I took after Tableau was the Google Cloud Platform Data Engineering Professional exam. This exam is not easy, and I highly recommend you take this if you have less than 2 years in your career. I still use a lot of the information that I learned from that exam. The certificate in this instance challenged me to learn the material thoroughly.
So, when is a certificate beneficial? It is beneficial when you use it to learn a new tool, framework, or cloud service and are at the beginning of your career. But more importantly, you should be implementing these newfound skills in a real project that you can showcase!
For your resume, I would put the certificates in a section by themselves below your education section which will be the last two sections in your resume.
If you have any questions you would like for me to answer, please comment here or message me on LinkedIn!
If you missed out on these two great reads from Joe Reis and Zach Wilson on Kimball’s way of data modeling, check them out here: