Data scientists and engineers are distinct but interconnected professions in the rapidly evolving landscape of data-driven businesses. Both roles are crucial in managing data and extracting its value, but their responsibilities and skill sets often differ.

A few years ago, the focus was primarily on extracting insights from data. As the industry matured, the importance of robust data management and the adage, “This shift in perspective brought the role and importance of data engineers into the forefront.

This guide will help you understand the differences between data scientists and data engineers, two of the most prominent careers in data science. Also, it gives all the information that helps you decide on the career you want to pursue.

Data Engineer vs. Data Scientist

The two roles are distinct and separate, even though there is some overlap between the skills of data scientists and engineers.

Role and Responsibilities

Consider data scientists and data engineers as complementary roles. Data engineers optimize and build the systems that allow data scientists to do their jobs. Data engineers manage vast amounts of data, while data scientists find meaning within them.

What is a Data Engineer?

Data engineers are data professionals who prepare the infrastructure of data for analysis. Data engineers are concerned with producing raw data and elements like formats, resilience, scaling, storage, and security. Data engineers are responsible for designing, building, and testing data and integrating, managing, and optimizing it. They also make the architectures and infrastructures that allow data generation.

They focus on building free-flowing big data pipelines, combining different technologies to enable real-time analysis. Data engineers write complex queries to make data easily accessible.

What is a Data Scientist?

Data scientists are tasked with finding new insights in the data prepared by data engineers. They conduct online experiments and develop hypotheses. Also, they use their statistical, data analytics, and data visualization skills, as well as their machine learning algorithms, to identify trends for the business.

The data scientist’s role is to engage with business leaders to understand their needs and then present complex findings verbally and visually in a way easily understood by a business audience.

Education and Requirements

Most data scientists and engineers have a bachelor’s in computer science, mathematics, statistics, economics, or information technology. While employers are often looking for candidates with advanced degrees, getting a job in data science or engineering without one is possible.

Requirements to Become a Data Engineer

Data engineers are usually software engineers with a Java, Python, and SQL background. Also, data engineering skills focus on mathematics or statistics, allowing them to apply different analytical methods in solving business problems.

Most companies are looking for candidates who have a bachelor’s in computer science, math applied, or information technology. Some companies may require candidates to hold data engineering certification, such as Google’s Professional Data Engineer, Associate big data engineer certification and IBM Certified Data Engineer. It is also helpful if the candidate has experience building large data warehouses and can perform some Extract, Transform, and Load (ETL) on top of massive data sets.

Requirements to Become a Data Scientist

Usually, data scientists are presented with large amounts of data and no specific business problem to solve. In this situation, the data scientist must examine the data, ask the right questions, and present the findings. Therefore, data scientists have many skills in big data infrastructures and machine learning algorithms. They must also keep up with the latest technology as they have to deal with data in different forms and formats to run their algorithms efficiently.

Data scientists should be able to program in languages like SQL, Python, R, and Java and have a good understanding of tools such as Hive, Hadoop, Cassandra, and MongoDB.

Which is Better, Data Scientist or Data Engineer?

The data scientist can only interpret data if it is in a format that he can understand. Data engineers’ job is to deliver the data to data scientists. However, data engineers’ demand is high because specific tools can’t perform the data scientist’s duties. In recent years, there has been a general belief that the need for data scientists will diminish as automation tools become sophisticated. This hasn’t happened (yet), and it may not.

Conclusion

The data engineers and scientists are two pieces of the puzzle to solving the data problem: Where do we get the data, and how can we use it? Both career paths have many crossovers — some data engineers leave the analysis and statistics behind to concentrate on the data pipeline. Some data engineers are curious about the fate of the data they store, so they look for opportunities to study more mathematics to enter data science.

Data engineering may be a better career choice for individuals interested in working with data infrastructures, optimizing performance, and building scalable applications.