Discover how Young Mavericks’ Data Engineering Traineeship inspired Kevin’s (32) career switch

Hey everyone, I’m Kevin Bowey. Thanks to Young Maverick’s October Data Engineering Traineeship, I am currently enjoying working as Junior Data Engineer. You might think to yourself: wow, starting a traineeship at 32? Hell yes! Many of my fellow trainees dove into the world of Big Data during or immediately after their studies. My path to machine learning, however, was a lot less straightforward – a ‘detour’ that gave me unique skills as a Data Engineer.

My Detour Towards Data

I discovered my passion for social communication mechanisms, and how these can be used to strengthen democratic processes, during the first year Philosophy and my bachelor’s degree in Political Science. I quickly realized the importance of programming, internet and software fluency in our digital age. I began to familiarize myself with programming languages ​​and processes fresh out of university. Shortly after, I started developing web applications as a Backend Developer. I learned a lot about digital communication during these years, which was really cool.

A Career Switch Calling 

When I wasn’t playing sports or hanging out with friends, my spare time was spent behind a computer. I like to stay up-to-date with the latest online articles, learn new tools, take professional courses and attend conferences to stay apprised of any new technological developments. Over the years, I became increasingly interested in the vast social networks connecting all of us – and the role data and machine learning play in this. Over time, this fascination became more than just a hobby. I had found my calling in the intriguing world of data technology, and I finally made the career switch.

Young Mavericks’ Data Engineering Traineeship

After some research on LinkedIn, I found Young Mavericks’ traineeships. I applied to become a Data Science trainee, but the introductory meeting with Vanity from Talent Acquisition taught me that this traineeship did not match my work experience or interests. The Data Engineering traineeship, on the other hand, turned out to be a perfect match. After an assessment and a few more interviews, I was hired and happily immersed myself in the exciting world of Data Engineering. I finally achieved my objective to work professionally with data and still do today. 

The next logical step

Looking back, I am not sure if I would have dared to make my career switch if I had not found this traineeship. Young Mavericks gave me a push in the right direction at just the right time.  Prior to the traineeship, I was worried I would be too far behind my fellow trainees. It soon became apparent that I wasn’t just able to keep up, but my years of working with web applications had actually given me a lot of great foundational experience with comparable technologies. The Data Engineering traineeship and the subsequent secondment is fascinating, the work is meaningful, and I thoroughly enjoy it.

Perfect for Professionals with Data-Driven Ambitions

I really like working at Young Mavericks! I enjoy the cooperation with my colleagues; everyone is very approachable and thinks through problems together. The role of Data Engineers is still evolving and varies with each new company. Young Mavericks’ traineeship teaches us to anticipate this: we explore the many issues and challenges Data Engineers encounter in a project. The traineeship also emphasizes where the Data Engineering and Data Science paths cross, and how to perform advanced analyses. I heartily recommend the Data Engineering traineeship to anyone who – just like me – dreams of a data-driven career.

Interested in Young Mavericks’ Data Engineering Traineeship? Learn more about our challenging program

ELT, ETL and Data Pipelines: loading Data in an Automated Manner and how to do it yourself

My name is Don and I am something between a data engineer and a data scientist. I automate repetitive tasks, generate insights from the data, manage projects and I often help in an advisory role for these processes. I especially enjoy the creative process required to solve problems using data.

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These instructions build on what has been discussed in ELT, ETL and Data Pipelines. In that guide, we discussed the problems that arise in storing and using data for a company. In response to those problems, we introduced the concept of Data Pipelines, which helps the company become better aware of the data loading steps and incorporating these steps in the most optimal way to create a Data-driven Culture. We also discussed some specific tooling that can be used to properly deploy Data Pipelines. 

Now that we understand the concepts behind Data Pipelines, we will now apply them to implement a functioning Data Pipeline. Just like most of our data engineering processes, we follow a step-by-step plan and provide an implementation strategy for each step. 

Hopefully a step-by-step plan will give you a solid foundation when you are constructing your own Data Pipeline as well as the implementation methods.  You can find the whole code on our Giftlab.

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