Data Engineer vs Data Scientist: What goes into building a successful data team?

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Rick-Deller

Technology, Open Source, Data...

Whilst the concept of Data may still seem relatively new, it is integrated into our everyday lives influencing how we eat, shop, date, exercise, drive, and work. Businesses across the world now utilise the vast amount of “big data” available daily for things such as trend analysis, information planning, customer intelligence and experience to name a few.

The importance of Data and Analytics is nothing new; however, it is becoming more and more important to businesses, with significant roles being created amongst the world’s leading companies. To put data in perspective, one study quoted by tech radar estimated that “by 2024, the world’s enterprise servers will annually process the digital equivalent of a stack of books extending more than 4.37 light-years to Alpha Centauri, our closest neighbouring star system in the Milky Way Galaxy.” That’s no mean feat.

But the value of all this data is only as good as the way it is pulled apart, analysed, managed and held thereafter. In order to do this, companies will need some of the brightest minds to create a successful data team to make sure the data is collated, analysed, and stored accurately to add value to the business.

So, what goes into building a successful data team?

In the same way that you can’t build a structurally sound house without any foundations or design plans already in place, you can’t effectively utilise, manage and store data without a team of talented data scientists and data engineers.

In a nutshell data engineers are the implementers getting their hands dirty (so to speak) building the pipelines and data structures ready to store the analysed information. Scientists on the other hand are the researchers and analysts, who gather and analyse the information, pulling the data apart. Having the two working in tandem enables a team to produce the best results in the shortest time frames.

I have found that some businesses in order to save time and money often try to have one role without the other, or they try to get a 2in1. In order for data to be utilised effectively and to the fullest, a scientist can’t work without an engineer and vice versa. They each rely on the other’s role to get the job done to the upmost efficiency and to implement the most creative of ideas.

So how does the role of Data Scientist and Data Engineer differ?

Data engineers are skilled in data warehousing, data streaming and programming to name a few. They essentially prepare the “big data” infrastructure to be analysed by the data scientist. As software engineers, they are tasked with designing, building and integrating data from various sources with the overall aim of optimizing the performance of the company’s big data system.

Like engineers, data scientists are expected to have strong skills in programming but their skills are purely analytical; turning raw data into insights. Their main function is to help businesses turn data into valuable insights whilst applying statistics, analytics and even machine learning to solve problems.

Big data requirement

From my experience, I have seen that some companies don’t truly understand their big data requirements. They are unsure whether they need a scientist or an engineer and look for a candidate that can be both: A Data Science Engineer. So what’s wrong with this job title? There are many crossovers within data science and data engineering, and for a team to work effectively there always needs to be collaboration between the roles, but one can’t be the other.

While the scientist and engineer might both have a good understanding of each other’s role, they both are passionate about separate things with different learning backgrounds. For example, data engineers often come from a software background, studying code and learning the fundamentals of warehousing data, whereas data analysts naturally progress to become a data scientist, learning the essentials of analysing and pulling apart data.

The Data Engineering talent shortage

There is a rising demand for data scientists with masses amounts of data being generated companies don’t know what to do with it all or where to start. So they place an emphasis on research and insights and look to hire the people that can make sense of it all and give it purpose. However, without the work of a data engineer, the work of data scientists is 50% less effective. Ideally, data engineers should be brought in to the data team first-hand to maximize the data scientists scope of research and analysis.

The impact of this interest in the data science field also means that the pool of talented data engineers has dried out, with demand exceeding supply. This demand not only comes from the increase of the big data stack and the move of data from being a by-product to an integral part of a company but also the new jump towards machine learning. It is also mainly down to the major companies such as Airbnb, Uber, and Spotify now building their own data products increasing their demand for engineers to build and maintain their systems.

This data engineering talent shortage is self-perpetuating due to the lack of interest and support for the craft, therefore the lack in talented engineers comes full circle. In order to combat this, businesses need to place an emphasis on supporting the craft and talent out there.

Due to the ever-evolving nature of data, businesses are often very strict in what they are looking for in a data engineer. They want a candidate that ticks all the boxes but there is a chance that they will miss out on talented and experienced engineers that are missing a programming language from their C.V (which they would otherwise be willing to learn and increase their skill set to succeed within a company.

Some small businesses and start-ups can’t afford the luxury of having both an engineer and a scientist and so try to look for a candidate that can do it all. However, there are companies out there that see the shortage in the market as an opportunity to hire and train engineers that are willing to learn new languages and even some analytical skills.

To build a successful data team that is valuable for their business not only do companies need to know the difference between a data scientist and a data engineer, they also need to understand how to get the best out of them. To lend a helping hand to the talent shortage, businesses need to be open-minded upskilling and training engineers. This requires bringing on a data engineer with potential and allowing time to train on certain elements that are needed for the role and to successfully build a data team.

Are you unsure about your big data requirement? Do you need help to build and structure a successful data team? Whether you’re looking for a Data Engineer or a Data Scientist we can help. Contact me to discuss further on johns@eligo.co.uk or call 020 8944 4197.

If you’re a Data Engineer or Data Scientist looking for your next role contact me to talk about the roles we have available or visit our jobs page.