Data Scientists vs Data Engineers: Know Which is Best for You?

IndustryWired explores data scientists vs data engineers to give a complete outlook on the job roles

Data scientists

Data scientists

Although fancy titles like data scientist vs data engineers are new to the world, we have been using big data for a long time now. Traditallly, anyone analyzing or dealing with data to reap benefits was called a ‘data analyst.’ On the other hand, those who created backend platforms to support data analysis were labelled a ‘business intelligence developer.’ But over the years, the scenario of gathering, examining, processing, analyzing, and acquiring answers from big data has drastically expanded, introducing new job opportunities in it.

Big data has changed the world upside down in recent years. Since the commercial sphere realized the importance of data, any profession or advantage around the technology has gained massive importance. Data scientists vs data engineers are no different. In 2011 and 2012, Harvard Business Review declared data scientists jobs as the ‘Sexiest Job of the 21st Century.’ Even after a decade, it is keeping its place in the market and has also introduced many other data-related professionals like data engineers and others. Although data scientists enjoy the limelight often, data engineers are the trigger behind handling big data effectively. Without data engineers, data scientists and analysts can’t prosper with a raw dataset. According to Glassdoor, the number of job openings for data engineers is almost five-times higher than the number of job openings for data scientists. In this article, IndustryWired aims to explore everything you need to know about data scientists vs data engineers to choose the best suited career option.


Data Scientists

Average Salary: US$100,560

Data science has emerged as a major technology that could revolutionize the whole commercial ecosystem. Since the gathered data had reached an unimaginable amount, professionals like data scientists have emerged to handle them effectively. Generally, data science helps design, product, and marketing teams to get the maximum out of big data. Design teams use big data to enhance their graphic capabilities while product teams use it to improve their product range and customer experience. On the other hand, marketing teams learn new ways to reach out to customers, thanks to data scientists. 

Roles and Responsibilities of a Data Scientist: In a nutshell, data scientists analyze data, create algorithms, and make predictions based on the data provided. It goes beyond the responsibilities of a data analyst. Generally, data analysts spend more time on cleaning and preparing data for the analytics process. But data scientists take big data in their hands and test and tweak them for answers. Therefore, data scientists should have basic programming knowledge and know programming languages like Python or R. Knowledge in machine learning algorithms, data visualization, and bid data is also mandatory. 


Data Engineers

Average Salary: US$119,885

While data scientists do the analysis work, data engineers play a big role in fishing for the right data and preparing it for the process. They focus more on the readiness of data and things like format, resilience, scaling, and security. Since gathering and formatting data needs both programming and basic statistical and mathematical capabilities, data engineers are mandated to have them all. They should also be able to connect data with business problems and make them good enough to solve it. 

Roles and Responsibilities of Data Engineers: Designing, testing, building, managing, integrating, and optimizing data are some of the usual tasks of a data engineer. They also do the internal data generation building process. With the help of big data technologies and analytics, data engineers create free-flowing data pipelines. However, despite their command over data, they are only asked to deal with a certain or a part of the data in a company. 


Where Does Data Science and Data Engineering Overlaps?  

At the core of data scientists vs data engineers, big data prevails. Besides, data engineers generally have a programming background with knowledge on languages like Java, Python, or Scala. On the other hand, data scientists have a stronghold in Maths, Statistics, Economics, or Physics.