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Data Engineer vs Data Scientist
Updated on 26th May, 23 992 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:

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What is a Data Science Engineer?

Data Engineers are the professionals who prepare large sets of data for analysis by Data Scientists. They are typically responsible for building data pipelines and collecting reliable data from various sources. Data Engineers form the base of the entire analytical architecture of an organization. These professionals are known for closely working with Data Scientists and assisting them in data analysis and operational tasks. They are skilled in Data and web service integration, database knowledge, data warehousing, and tools like Hadoop and Spark, etc.

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Key Differences between Data Scientists and Data Engineers

Now let us understand what are the points of difference between Data Scientists and Data Engineers.

S.NoData EngineerData Scientist
1They are responsible for creating the entire data architecture.Data Scientists build on the data architecture created by the Data Engineers.
2The functions performed by Data Engineers are Designing, Building and Arranging data.Their functions include Analyzing and testing the data along with Deriving valuable insights and presenting them to the management.
3They understand the business requirements from the senior level managers and other non-technical stakeholders.They set their goal and identify the business needs based on the data given by Data Engineers
4Data Engineers have no hold in the decision-making.Data Scientists play an important role in the business decision making as their analysis and insights are leveraged by the management.
5These professionals work on raw dataData Scientists work on the data manipulated and shared by Data Engineers.
6Data Engineers specialize in tools such as MySQL, Hive, Cassandra, Oracle, Sqoop, Riak etc.Data Scientists are skilled in using tools like R, Python, SAS, Julia, SPSS and other programming languages.

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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.

Data Engineer vs Data Scientist

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

What does a Data Engineer do?

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. The role performed by a Data Engineer can be divided into 3 categories:

  • Design
  • Build
  • Arrange

Design: They design the entire data architecture on which the Data Scientists work. They work on structured as well as unstructured data and create the base for data scientists to perform analysis and interpretations.

Build: Data Engineers build data pipelines by collecting data from various sources. They are responsible for converting the data into a usable format and implementing and maintaining analytics databases.

Arrange: Data Engineers organize the data so that it can be used in specific analytics applications. Also, they are responsible for performing data cleaning and consolidation.

To be precise, the role of a data engineer can be summarised as follows:

  • Performing data extraction from various reliable sources such as official websites, journals, 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

A Data Engineer works on the improvement of data based on the following criteria:

  • Reliability
  • Efficiency
  • Availability
  • Quality

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What does a Data Scientist do?

After getting the processed data, the role of a Data Scientist comes into play. 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.

The role of a Data Scientist is explained in the following steps:

  • Analyze
  • Test
  • Derive Insights
  • Present

Let us now understand them one by one.

Analyze: Data scientists perform analysis on large sets of complex data to recommend and prescribe the right course of action for significant solutions to business problems.

Test: Data Scientists are also involved in testing whether the applications or models are meeting the business needs and requirements.They monitor the performance of the models to identify whether the goal is being met or not.

Derive Insights: Data Scientists make sense out of the data by identifying trends and hidden patterns in relation to the business goals or problems.These derived insights are used by the management for a data-driven business decision-making process.

Present: A part of the job role of Data Scientists also includes data visualization. Data Scientists communicate the insights and findings to the management in a lucid and understandable language. These skills are collectively called data visualization skills.

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Let us understand the roles of Data Scientist through an example. Consider a Data Scientist in an oil and petroleum company who wants to work on the data about the availability of expanding the industry to the Middle West. So the first step that he would take will be to approach the Data Engineer for information regarding geographical topology, government policies, etc. After that, he would apply certain data techniques to generate insights from this data and present them to the top management so that they can make a decision whether to go on with the expansion or not.

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.

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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Career Transition

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.

Tools, Languages, and Skills


The tools used by Data Engineers are:

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

The tools used by Data scientists 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. let’s further check out the best languages used by them.

The languages for Data Engineers are:

The language for Data Scientists is:

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

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


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:


The Data Engineer profile requires you to have an in-depth understanding of different programming languages, such as SQL, Java, SAS, Python, etc. 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.

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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 of 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.

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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 for 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 Salaries based on their salary slabs:

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Data Engineer vs Data Scientist Salary

Below discussed are the salaries earned by both Data Engineers and Data Scientists.

Data Scientist Salary:

Data Scientist Salary

Data Engineer Salary:

Data Engineer Salary

The highest-paid job, no doubt, is a Data Scientist profile, the Data Scientist’s salary 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 a 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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