Data Engineer vs Data Scientist: Ultimate Comparison Guide [2024]

Data is the new trend, and data professionals are in constant demand by organizations. However, the main question or confusion individuals face is to choose between data engineer vs. data scientist. Most often, individuals feel these two fields are the same and lack clarity on how they differentiate. This article will discuss how these two are similar or different in terms of various aspects.

Is There Any Difference Between a Data Engineer and a Data Scientist?

At one time, data scientists were made to work and perform the responsibilities of data engineers. However, as the area of data has expanded and changed—data collection and administration have become increasingly intricate and unmanageable. Businesses are demanding more insights and answers from the data they collect—the work has increased and thus divided into two distinct roles.

Thus, the main difference between the two positions is that a data engineer creates and maintains the structures and systems that keep, extract, and organize data. Whereas, the data scientists analyze the data to identify trends, offer business insights, and answer queries related to the organization.

Who is a Data Engineer?

A data engineer is a professional who plays a crucial role in preparing and managing data for analysis. They are responsible for creating, constructing, testing, and maintaining complete data architectures, which include databases and large-scale processing systems. These professionals work to ensure that data is accessible, reliable, and secure.

They build the infrastructure required for optimal extraction, transformation, and loading (ETL) of data from various sources. Data engineers also collaborate with data scientists and analysts to understand data needs and implement the necessary solutions to support data-driven decision-making in organizations.

Who is a Data Scientist?

A data scientist is a professional who works with and analyzes the data provided by data engineers. They play a critical role in interpreting complex datasets to uncover patterns, trends, and insights that can inform business decisions. Data scientists are adept at organizing and managing large volumes of data, often referred to as “big data.”

They utilize various tools and techniques, such as machine learning, statistical analysis, and data visualization, to transform raw data into actionable intelligence. By doing so, they help organizations make data-driven decisions and solve complex problems. Data scientists often communicate their findings to stakeholders through reports and visualizations, making complex data comprehensible and useful for decision-makers.

Data Engineer vs Data Scientist: Role Difference

Let’s understand how their role differs.

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A data scientist deals with the below:

• Data visualization
• Mathematics
• Model building
• Machine learning
• Communication and team management
• Statistical tools

On the other, a data engineer works on:

• Improving data accessibility
• Programming languages
• Helps in enhancing the organization’s efficiency
• Database management
• Software oriented issues
• Data pipelines

Each job has a different role, however, there can be some common things between them as well. There can be shifts in responsibilities which may depend on industry, business, and unique situations. Data scientists help solve business situations and come up with solutions using analytics. While, data engineers generally help to get information needed for analytics.

Both data scientists and data engineers work in harmony using algorithms to create business relations. Each job role has its capacities and limitations. However, they both work towards one common goal.

Data Engineer vs Data Scientist: Skill Set Difference

There are different skill sets needed for both roles. Listed below are the technical and soft skills required to become a data engineer and data scientist.

Key Differences Between Data Engineers and Data Scientists

Here are the key differences in these two roles:

Data Engineer vs Data Scientist: Salary Difference

There is a huge demand for data engineers and data scientists. As per the US Bureau of Labor Statistics, there can be an expected growth of 31.4% in data scientists and 10% in data engineers jobs by 2030. Let’s look at the basic salary range for these positions:

Data Engineer vs Data Scientist – Career Path and Future Prospects

There is very little chance of getting a data engineer position at an entry level. Data engineers mostly evolve from software engineers to business intelligence analysts. These professionals move to a data engineer position after enhancing their skills. The prospects of a data engineer are becoming a manager or a senior manager, and later skill enhancement can take up to a data scientist, chief data officer, or data architect role.

Data science, on the other hand, is constantly evolving. A data scientist can work in any field, such as healthcare or finance. If you have the right aptitude and analytical skills, this is the perfect field for you.

Educational Qualification for Data Engineer and Data Scientist

For data engineers, they must first complete a bachelor’s degree in a related field, such as computer engineering, software engineering, or mathematics. The chances of getting a competitive job increase with a master’s degree; however, it is not a mandate. Some universities even offer postgraduate degrees for becoming a data engineer.

Besides, there is always an option to go for an online course, such as a data engineering bootcamp from CCSLA. A lot of companies look for professionals with formal education and certification for the data engineer profile.

A data scientist must have a bachelor’s degree in fields, such as data science or similar. In case you want to specialize in a particular area, you can also opt for a postgraduate degree. Most universities also offer a formal degree with internship opportunities that can help enhance the skills needed to become a data scientist.

However, it is not necessary to have a degree to become a data scientist. There are entry-level jobs in the broader domain that can lead to a data scientist position. Like data engineers, scientists can also enroll in data science courses online, enhance their skills, and get ready for a competitive journey.

Skill Similarities in Data Engineers and Data Scientists

Since both these roles have a similar focus on big data, they also have certain skills that they use that are identical. This is the reason why data engineering is often considered under data science. Here are some skills that overlap:

• Data analysis – Data scientists are experts in analyzing data as it is their core job. However, even data engineers are required to have a basic understanding of data analysis. This helps them in planning their tasks and understanding how the data will be used.
• Big data – Both data scientists and data engineers work with big data. The only difference lies in how they use it. Data scientists analyze this big data, where the engineers build big data architectures. Hence, both these job roles require an understanding of working with raw and unstructured data.
• Programming – Data engineers who have a background in software engineering are experts in programming. Even though a data scientist does not need a lot of programming skills, it is still a prerequisite.

Data Engineer vs Data Scientist: Which is a Better Career Option?

A data scientist can only use data if they receive it in a proper format. It is the job of a data engineer to get this data to the data scientist. Therefore, at present, there is more demand for data engineers as tools are not capable of performing tasks done by them.

It was formerly widely held in the industry that the demand for pure data scientists would decline as increasing automation techniques were created. However, it hasn’t happened yet and might not.

Can a Data Scientist Become a Data Engineer?

A data scientist can become a data engineer; however, they must acquire additional knowledge and skills that are related to data engineering. These two are closely related fields within the broader umbrella of data analytics. Still, they focus on different life cycles of data and have distinct skill sets.

Challenges Faced by Data Engineer and Data Scientist

Both data engineers and data scientists face challenges while on their jobs. The data engineers have to tackle challenges related to scalability, data integration, data security, and privacy. Thus, they must constantly update themselves with evolving technologies.

On the other hand, a data scientist has to acquire high-quality data, choose the correct algorithm, and communicate insights effectively to stakeholders.

Can Data Engineers and Data Scientists Work Together?

Both data engineers and scientists can comfortably work and collaborate because of similarities in their abilities in programming and data pipelines. While data scientists are more focused on creating and evaluating hypotheses using data, data engineers are more concerned with the architecture and infrastructure that enable data scientists’ work. Data scientists and data engineers collaborate in this way.

In today’s larger businesses, you’ll frequently find a mix of data scientists and data engineers working with data. Scientists and engineers likely have the same ultimate goal in mind: effectively utilizing data to further their institutions’ missions.

Final Thoughts

This article has discussed the differences between data scientists and data engineers in detail. You can decide the role that fits the best as per your interest. If you are interested in building and maintaining infrastructure that supports data analysis, go for a data engineer role. Or if making predictions interests you, a data scientist is the perfect option for you.

Irrespective of which job role you decide and choose, CCSLA has the best training program for your needs. For instance, you can enroll in the data science and data engineering bootcamp and get ready for a career in data in just 12 weeks. Remember, whichever job you choose, each has its pros and cons, skills, and responsibilities. So, choose with full consideration.

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