Whether you’re reconsidering your own career path or you’re planning to build a team to manage and analyse big data, it’s extremely important to fully explore and understand the field to avoid any confusions. The importance and meaning of data science have been constantly growing and changing for some time now. The new roles have emerged becoming the subject of a very much heated discussion. The differences are not always clear for everybody, especially when it comes to data scientists and data engineers. They come from the same background, but the responsibilities of one person needed to be split in order to efficiently deal with the growing amount of complicated data. There’s still a significant overlap in skills and tasks but their focus and interests are now in two entirely different places.
The Requirements For A Data Engineer
To find a data engineer, employers look for a person who graduated in computer science, applied math or information technology; sometimes they require some data engineering certifications as well. But in reality, this profession is all about the right skills – building and optimizing data pipelines from scratch, solving complex problems, building big data warehouses to run Extract, Transform, and Load or ETL on top, and programming languages, such as Python, Java, C++ and Scala.
The Requirements For A Data Scientist
Most employers seek data scientists with something more than just a bachelor degree (for example, James Cook University Online offers a graduate degree that you can complete online without being forced to change your whole life if you already have a job or a family). Degrees in mathematics and statistics, computer science or engineering can prove to be helpful, too. However, many companies feel like they don’t have a lot of choices because of the current high demand so very often, they decide to hire people without a demanded graduate degree.
Data scientists need to possess great knowledge in big data infrastructure, machine learning, statistics and data mining in order to not only explore the data but also to find the right questions on their own and come to conclusions as very often, they are simply presented with some big data without any specific issues to solve. It means that they also have to be able to stay up-to-date with technology if they want their algorithms to be effective. They need to be familiar with the fundamentals of computer science, programming, different languages (Python, R, Java, MATLAB, Scala, C, SQL) and database technologies, although it’s not required for them to be as proficient as data engineers.
The Responsibilities Of A Data Engineer
A data engineer is someone who prepares the whole data for analysis. They design, build, test, integrate, manage and optimize all the data flowing through a company, as well as help to generate some. Scaling, security, resilience or formats of different sets of data are among the many things that they deal with daily. Also, they need to be proficient in programming languages, that’s why they very often start as software engineers. They can develop and manage systems to prepare them for the analysis of bigger sets of data – they simply help data scientists to read flooding data and present the company with specifics like insights and conclusions. Thanks to their queries, the data is easily accessible for everyone.
The Responsibilities Of A Data Scientist
A data scientist is described these days as a data analyst driven by computer science and machine learning. They used to build the infrastructure and clean up the data but now it’s part of the job of a data engineer. Thanks to that, they can work with organized sets in order to find some new insights. Some tasks may be overlapping but you definitely can’t replace a scientist with an engineer, nor the other way around. They may both be programmers, but a data engineer is more focused on these programming skills while a data scientist is more of an analyst who only needs to program to help yourself with some more complicated analysis. To put it in a simple way – a data scientist analyse the data that a data engineer generated or organized.
A data scientist needs to see the bigger picture everywhere and they tend to be more creative – very often, they look for new opportunities for their company by researching markets and trends. They work with the data infrastructure, but they don’t build or maintain it anymore (that’s what a data engineer is for). They stay in contact with company executives to better understand their needs and expectations to be able to connect an enterprise with its customers, so it’s important for a data scientist to have well-developed communication skills as well.
The Salary Of A Data Engineer
The average salary of a data engineer is estimated at about $142,000 per year. Of course, everything depends on their location, experience and the role they have to play in a company.
The Salary Of A Data Scientist
It’s the same case – it all depends on their skills, qualifications, location and type of the job, but the salary of a data scientist is roughly calculated to be $139,000 annually.
However, both of these professions are in high demand right now and their importance is growing so you can expect that the salaries will grow in the future as well.
As you can see, data scientists and data engineers aren’t really competing with each other, so it shouldn’t ever be data scientists vs. data engineers. Both of these professions come from the same background, they used to be one and now they complement each other, and need to cooperate in order to help their company achieve the desired goals. No matter if you want to become one or the other, or you look for some professionals to gather a team – there hardly are specialists who can be both. You’re either a good data scientist or a reliable data engineer – unless you’re aiming at a career in machine learning but that’s a totally different story.
