In this Big Data era, data scientists are in high demand and a huge variety of people are stepping up, with backgrounds ranging across disciplines like Maths, Physics and Computer Science. Data scientists are the minds behind every iteration of the ‘next big thing’, be it AI, machine learning or new algorithms that are helping to solve everyday problems.
But while it might be appealing to go down the data science track, there’s a lot that can be said for the data engineers, who are at the core of most enterprises.
While the data scientists are analysing, modelling and developing the overall picture of what truths are being revealed by data-driven insight, it is the engineers who are enabling them to do so, by creating software, processing data, integrating systems and ensuring that everything that is needed to produce the best possible analysis is there.
In theory, the two disciplines are supposed to be separate, two different paths to travel down in a data enterprise. In reality, there is significant overlap between the skills, abilities and knowledge needed, helping to develop better understanding of the entire data process from start to finish.
However, because the process of data analysis begins with the engineers, it is much easier for them to be able to adapt and assimilate into various roles within either discipline, hence why a data engineer is more likely to be able to develop data science skills, while the same might not necessarily be true the other way around.1.
Here are 4 main reasons why developing an understanding of data engineering can help you to become a better data scientist:
1. Adaptability and value
Many smaller companies don’t have the budget for both data scientists and data engineers. By developing the technical skills of the engineer, you can make yourself a more attractive candidate, as someone who can adapt and switch between both disciplines with ease. In essence, it is upskilling, learning the entire trade from top to bottom so that you can show greater flexibility and understanding of the data journey, from raw data to honed analysis and, as a result, make yourself much more valuable to a company.
2. Reaching for greater challenges
While much of data science is considered ground breaking and innovative, every data scientist will agree that there are more mundane parts to the job, such as performing routine data operations. What’s more, for many data scientists, this will form the bulk of their work. As an engineer, your focus would always be on building more effective models and infrastructure, tasks that will constantly challenge your abilities, instead of allowing them to be spent on more mundane tasks.
3. Becoming more ‘hands on’
As a data scientist, you are only ever looking at a top-side view of what the company does. But as an engineer, you will be dealing with the crucial infrastructure that channels the raw data – looking ‘under the hood’, as it were. You’ll be developing unique problem-solving capabilities as you strive to understand what is going on and how to make it work better, truly the closest you can be to the raw action as is possible in a data-driven enterprise.
4. Creativity, innovation and problem solving
Whether you are a scientist or an engineer, problem solving will always be a crucial component of your skills base. As an engineer, you are afforded a whole range of situations that will allow you to develop this skill into your most valuable asset, as you consider the complications and issues of scaling and running a data pipeline, making sure that it can continuously operate without the need for your interference and developing a greater understanding of the physical aspects of the data flow. In building bespoke solutions, creativity will play the biggest role, as you search for innovative formulas within constantly evolving environments.
There’s a lot more to being an engineer than many people think, and, in developing the skills and knowledge to be an engineer, as well as a scientist, you will allow yourself to become an integral and valuable asset in any data enterprise, understanding the process from start to finish. So, the only question left to ask is, why not do both?
By Michael Reed, Starcount’s Head of Data Science Engineering