Data Engineer vs Data Scientist
Updated on 20th Dec, 21 376 Views

Before exploring more about these trending professions in our blog on Data Engineer vs Data Scientist, let’s look at the topics covered in this blog:


Key Differences between Data Scientist and Data Engineer

Many times, people get confused with both terms and end up using them interchangeably. Let’s have a look at this Data Engineer vs Data Scientist comparison and learn how they are different from each other.


Data Engineer vs Data Scientist: Job Roles and Responsibilities

In the comparison of Data Engineer vs Data Scientist, you need to remember that both the roles have their respective responsibilities in the field of data, but a Data Engineer handles the first operation on the raw data before transferring it to the database of the organization.

Difference between Data Engineer and Data Scientist on Roles and Responsibilities

Let’s now check out the difference between Data Engineer and Data Scientist roles.

Data Engineer (Design > Build > Arrange)

Data Engineers are often sort of Data Architects. They are the masters of designing, building, testing, integrating, and optimizing the raw data for operational or analytical purposes from a variety of sources.

For example, in a car manufacturing firm, a Data Engineer figures out the data that is to be stored in a data schema. Then, he looks for the storage space, also known as Data Warehouse, which is secure, accessible, and reliable, and after that, he implements the system (the ETL process) in the following manner:

  • Extract the data from different sources, such as different automobile websites, official websites of car manufacturers, etc.
  • Transform the data by converting it into an appropriate format and cleaning it by dropping the unnecessary and unwanted elements
  • Store the data in the warehouse

The most significant role of a Data Engineer is to build a free-flowing data pipeline by merging different data technologies and work for the improvement of data based on the following criteria:

  • Reliability
  • Efficiency
  • Availability
  • Quality

Data Scientist (Analyse > Test > Derive Insights > Present)

After getting the processed data, the role of a Data Scientist comes into play. A Data Scientist is responsible for extracting the insights out of the data with the help of advanced data techniques. The Data Scientist uses techniques such as clustering, decision trees, neural networks, etc. that will bring magic to the whole process and, eventually, aid the organization as it will directly impact the decisions and help identify new trends and opportunities and know about customers and the areas of improvement. These professionals indulge in conversation with business leaders to understand specific demands and work with their domain expertise to achieve the desired goals.

For example, a Data Scientist in an oil and petroleum company wants to work on the data about the availability of expanding the industry to the Middle West. So, he first approaches the Data Engineer for information regarding geographical topology, government policies, etc. After that, he applies certain data techniques to generate insights from this data and presents them to the top management so that they can make a decision whether to go on with the expansion or not.

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Data Engineer vs Data Scientist: Education Background

As both job profiles complement each other, there is one thing in common: If you want to pursue any of these profiles, you should have a background in computer science.

There used to be a requirement for a dedicated skillset as both fields required domain expertise, but now, it is uncommon as there are ample examples of people coming from various other backgrounds, such as biologists, meteorologists, or physicists, making a career in the field of data.

Difference between Data Engineer and Data Scientist on Education Background

Data Scientists often have skills in mathematics, statistics, econometrics, and operations research. They are more inclined toward the business-oriented side than Data Engineers.

Data Engineers usually have a pure engineering base as they are responsible to store and manage data efficiently in systems.

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Data Engineer vs Data Scientist: Tools, Languages, and Skills

Various skills, tools, and languages are considered weapons for both Data Engineers and Data Scientists. However, there may be some differences here as well. Next, let’s explore what is inside the toolkits of these professionals.

Difference between Data Engineer and Data Scientist on Tools


The tools used by Data Engineers are:

  • Oracle
  • SAP
  • Cassandra
  • Redis
  • Sqoop
  • MySQL
  • PostgreSQL
  • Riak
  • Hadoop
  • Neo4j
  • Hive

The tools used by Data Scientist are:

These tools play a significant role in the comparison of these careers.


As coding plays an important role in implementing systems, Data Engineers and Data Scientists both should be proficient in certain programming languages. In this Data Engineer vs Data Scientist comparison, let’s further check out the best languages used by them.

The languages for Data Engineers are:

The language for Data Scientists are:

  • Python
  • R
  • Java
  • Scala
  • C
  • SQL

Python and R are no doubt the most popular among all of the above languages.

Certification in Bigdata Analytics


It is really important to upgrade yourself with the desired skills to be ready to enter this world of competition. Let’s check out the comparison between Data Scientist vs Data Engineer skills:

Difference between Data Engineer and Data Scientist on Skills

The Data Engineer profile requires you to have an in-depth understanding of different programming languages, such as SQL, Java, SAS, Python, etc. and in addition to that, you should also be a master at handling frameworks such as MapReduce, Hadoop, Pig, Apache Spark, NoSQL, Hive, Data Streaming, and others. You must also have a logical aptitude, organizational and management skills, leadership skills, etc., and you should be a team player who can coordinate with other members and with different teams.

A Data Scientist requires to have expertise in the field of mathematics, statistics, and probability, in-depth knowledge of programming languages, such as Python and R, and good command in visualization and extracting tools. As a Data Scientist, you should possess broad knowledge in the field of Machine Learning and Deep Learning as they will help you come up with high-value predictions that ultimately lead to better and smart decision-making. You should also have good communication, management, and presentation skills to present and convey the results of your analysis to the higher management and other stakeholders.


Data Engineer vs Data Scientist: Career, Salary, and Hikes

As the field of data is growing at an enormous pace, it has created a large space and opportunities for professions related to data. Forbes claims that the Data Engineer and Data Scientist jobs are emerging as top-ranking around the world. Harvard stated that Data Scientist jobs are the sexiest jobs of the 21st century.

Companies such as Facebook, Intel, Microsoft, S&P Global, Schneider, Moody’s, Amazon, etc. are interested in hiring Data Scientists on good pay. On the other hand, tech giants, including Google, Apple, Cognizant, Walmart, and others, are offering high-pay jobs to Data Engineers.

Let’s have to look at the difference between Data Scientist and Data Engineer based on their salary slabs:

Data Scientist Salary:

Data Scientist Salary

H4: Data Engineer Salary:

Data Engineer Salary

The highest-paid job, no doubt, is a Data Scientist profile that draws between US$4,33,000 and US$9,50,000 per year, with 0–4 years of experience. Whereas, the salary of a Data Engineer lies somewhere between US$116,000 and US$60,000 per year according to Glassdoor. These salary slabs are highly lucrative considering the fact that they are offered at the entry-level.


Data Engineer vs Data Scientist: Which is a better career?

When it comes to the comparison of Data Engineer and Data Scientist, you need to keep in mind that both roles have their own importance in the field of Analytics. You can also say that the difference between Data Engineer and Data Scientist roles does not affect their mutual impact on the field of data as both need each other for achieving the common goal of processing the data in an efficient and business-oriented way.

Ultimately, there are ample opportunities for both Data Engineers and Data Scientists, and this scope is always proportional to the growth of the data. So, upskill yourself to experience an exciting and high-paid career in the field of Data Analytics.

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