From a sports degree to a career in data engineering… my career pivot

Sidetrade Tech Hub
6 min readOct 4, 2023

Sidetrade data engineer, Lewis Thorpe, shares his experience pivoting from a sports degree into a rewarding data career.

Portrait photo of Lewis wearing a suit
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So, you have an interest in data and want to get a foothold in the industry, but you have no idea where to start? Well, I’ve been there before!

In such a fast-paced field, it can be a daunting task figuring out where to even start. And especially if your academic background is in a totally different industry.

I initially studied Sports Strength and Conditioning at both undergraduate and postgraduate level with the aim of eventually working in the world of sports.

But by the end of my post grad, I realized it wasn’t the industry for me.

I’d always had an interest in data, and whilst my studies incorporated some element of data analysis, it wasn’t enough to satiate my appetite.

So, I decided to do something about it.

What started the journey

This odyssey of curiosity into the world of data was born out of a late-night cinematic experience during the infamous 2020 lockdowns that everyone reluctantly endured.

For the inquisitive among you, the film that sparked my fascination was “Transcendence” with Johnny Depp in the lead role. The crux of the story revolves around an AI researcher who has his consciousness transferred into an AI system. Although I had watched this film before, the endless hours spent cloistered indoors undoubtedly magnified my interest in, well, just about anything that could occupy my restless mind.

Initial first steps

So, I figured, why not dive in headfirst? Back then, I was pretty much a blank slate when it came to data — I was aware of some statistical analysis methods I had covered at university but that was all I knew … daunting I know!

The initial reading I had done suggested it would be beneficial to get a good foundation in the Python programming language with data & AI based libraries while also taking time to learn the theoretical concepts that underpin a lot of the methodologies.

I just started out initially by stumbling through tutorials on some Python and data related concepts. Victories were achieved from any little bug or error I was able to overcome in tutorials. Ultimately, I found it more interesting and rewarding to start a project I was interested in and learn some of the concepts as I went along.

This worked for some of the Python and data concepts, however there is a significant amount of theory in AI, so I was doing background learning on this mainly through watching videos and reading articles. I particularly recommend anything on Towards Data Science!

The return to education

Photo of a book titled “Transformers for Machine Learning”
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Having already studied up to a postgraduate level at this point in an unrelated field, I figured I needed to be certain this was right for me, as I would need to fund it myself working in a full-time job. I had made the decision that if I still had the passion for Data and AI after a year of personal development, I would look into formal education on the subject to expand my knowledge.

Fast forward a year I decided it was time to bite the bullet and apply for a position on remote MSc in Data Science and Artificial course.

Up until the moment I started the course, I was fully self-taught in Python which is the programming language used for the course. The first assessment I took was based on data programming with Python and I was excited to have my work assessed on a topic I had devoted the past year of my life to.

This also inherently carried an element of pressure as I was hoping the time spent in my own personal studies had not gone to waste.

The grade I received is still to this date the highest grade that I’ve received in any formal education so far and effectively served as an affirming moment of validation in the self-development I had committed to. It was also the first time that the prospect of working with data crossed my mind more seriously.

Enter Sidetrade

Group photo of Sidetraders at SideHack 2023
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During my MSc, I came across Sidetrade — a global AI company with offices in Birmingham. They were running their 2021 Sidetrade Code Academy which is a free four-week coding course to instill basic programming skills into the participants and offer full time jobs at the end in either their R&D or professional services teams.

Graduates from previous years have gone on to enjoy successful careers at Sidetrade in software engineering, QA testing, data engineering and more.

Unfortunately, my study commitments meant that I couldn’t join Sidetrade via the Code Academy route as a full-time employee straight away, but lucky for me, one year later they were hiring for a Data Engineer and I got the job!

Overall, the responsibility of Data Engineering within Sidetrade is to ensure that the most recent advances in data related technology are being utilized. This allows us to empower our colleagues with robust data infrastructure while also contributing to features that enrich the customer experience.

The journey so far has been great with lots of interesting technology and concepts being covered such as:

Utilizing a variety of technologies is indeed exciting, but the real rollercoaster emerges when we harness their combined potential to empower our team and deliver substantial value to our customers.

As magnificent as these concepts are on their own, they get more exciting and interesting when used with a concept coined in the industry as a Data Lake. Effectively a Data Lake is a centralized repository that allows for the storage, retrieval, and analysis of large volumes of raw and structured data from different sources. The very features of implementing a Data Lake architecture provide significant value to an organization.

The Sidetrade Data Lake is a repository of over 593 million B2B payment transactions, valued at $4.6 trillion, from 20.7 million companies worldwide. It employs algorithms and machine learning to analyze and enrich this data. Through Sidetrade’s AI, Aimie, the Data Lake provides predictive insights on buyer payment behaviors, enabling companies, governments, and analysts to make data-driven decisions for cash optimization and credit management, especially in times of economic uncertainty. Its interactive map offers users aggregated, anonymized global payment trends, aiding in effective decision-making and negotiations.

While I have been proactive in trying to learn about relevant concepts, it would be naïve to say I’ve picked these up completely independently. I’ve had some fantastic support from my fellow Sidetraders, without whom I wouldn’t have made as much progress as I have so far in both my technical abilities but also in my softer business-related skills too.

It’s been especially useful when giving presentations on both my own and squad work and has allowed me to be able to build good rapports with my colleagues. I believe this has helped me with the development process as I’ve had a strong support network around me and naturally learned by collaboration with my colleagues.


So to summarize where I’m at now, I’ve been employed as a Data Engineer at Sidetrade for about a year. It’s been a very productive start to working in the tech sector so far, I’ve got to cover some exciting technology, collaborate on some interesting projects and even take part in a hackathon abroad! My journey into data has been a persistent one and I am intending for it to stay that way!

I’m proud seeing the hard work I’ve put in over the past 3 years come to fruition in my current position. It sure is rewarding to work in a role focused on a subject I am passionate about. I recognize there is still a lot to learn and I’m looking forward to seeing what the future brings from here!

If you enjoyed reading about my journey and are interested in forging your own career in an exciting AI company, then check out Sidetrade’s Careers here and make sure to give us a follow!



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