Data Engineer Vs. Data Scientist: What’s the Difference?

The use of data science is seen as a major career factor for many people. There are many roles in this area and it is very important to be aware of them when considering a career in this area. The data engineer and the data scientist are two important roles in this field.

In this article, we will discuss the differences between these two professional roles that you should be aware of. Both have their own requirements, responsibilities, and skills to help companies improve their business. If you are planning to work on one of these items, this article may be useful to you.

Who is a data engineer?

A data engineer is a professional who creates the data infrastructure and architecture for the analysis. The main purpose of this professional is to provide the necessary data and elements. Data engineers generally belong to the domain of software engineering and thus master various programming languages such as Python, Java, Scala, etc. In addition to their programming skills, some data engineers also have a degree in mathematics and statistics. This allows data engineers to work in different analytical roles. Although they have expertise in large data analyses, their main goal is to help scientists work effectively with large data sets.

Data engineers create a scalable and understandable infrastructure with raw data to provide valuable information to scientists. They also carry out complex analytical projects and create practical analytical solutions. Simply put, data engineers work in support of the analytical tools used by data scientists.

A scientist after

Who is a data specialist?

Scientists focus on discovering new possibilities made possible by the data infrastructure developed by engineers. In this way, scientists and data engineers work together to achieve the company’s objectives. In addition, prior to the creation of the position of Data Engineer, the work of the Data Engineer was carried out by Data Scientists. Although they work together, their roles are not identical. Just like data engineers, data scientists have programming skills, but data engineers have more experience in the field than data scientists. In principle, data specialists acquire programming skills to solve their complex analytical problems.

In general, these professionals have the best data analysis skills that engineers do not need. Although they need a data infrastructure to do their job, they have nothing to do with building and maintaining that infrastructure. In particular, Data Scientists are responsible for managing companies and markets at a high level using a variety of machines and methods. They use various analytical tools such as Hadoop, SPSS, advanced statistical modeling, and R. It can be said that scientists depend on data engineers for their work.

Operating role

The role of the data engineer :

Data engineering professionals who design, build, test, integrate, manage, and optimize data from multiple sources, building infrastructures. The main motto of data engineers is to create a continuous flow of data by integrating different technologies. You also create composite queries to facilitate access to the data. In principle, it is the task of data engineers in an organization to manage the flow of information in the system.

The role of the computer scientist:

The main task of the Data Scientists is to analyze the data infrastructure created by the Data Engineers. They have skills in mathematics and statistical analysis, making them well suited for business and market management. Data specialists conduct online experiments to create custom data products to improve business operations. They understand the needs of business leaders by connecting with them and presenting their results in an easier way so that the average business audience can easily understand them.

Level of education required:

In general, you can see that computing is a common educational base for scientists and data engineers. Compared to data engineers, however, data scientists have expertise in statistics, mathematics, operations research, and economics. The training requirements for both jobs are explained in more detail below.

Training requirements for the position of Data Engineer :

To work as a data engineer in a company, you must have a bachelor’s degree in mathematics, computer science or information technology. Besides a license, it’s a good idea to have extra certifications in data engineering to get this job. Below are the training requirements for obtaining a job as a process engineer.

  • Bachelor’s degree in statistics, computer science, information technology, or another equivalent subject.
  • Experience with cloud-based data solutions such as AWS, EMR, RDS, Redshift, EC2, etc.
  • Experience with analysis tools, automation, configuration management, system monitoring, dashboarding, and external and internal cause analysis.
  • Five years’ professional experience or three years’ professional experience at the university level.
  • Knowledge of programming languages such as Java, C++, Python, Scala, etc.
  • Knowledge of various databases and experience with relational databases
  • Experience in the construction, management, and optimization of large data infrastructures and pipelines.
  • Strong analytical and management skills to deal with unstructured data.
  • Knowledge of SQL for writing and debugging

Training requirements to work as a Data Scientist :

As a rule, companies prefer candidates with a Ph.D. or Master’s degree to work in the IT field. In addition, candidates with a degree in computer science, mathematics, and statistics are suitable for the position. Since scientists work with different datasets and analyze them, they need to be familiar with the data infrastructure, machine learning, statistics, etc., and they need to be able to use the data in their work. Below you will find everything you need to apply for a job.

  • D or a master’s degree in mathematics, computer science, engineering, statistics, or another equivalent subject.
  • Excellent analytical and mathematical skills
  • Experience with data mining technologies, machine learning, including neural networks, learning from the decision tree, clustering.
  • Experience with cloud databases containing large amounts of data
  • Knowledge of programming languages such as Scala, Java, MATLAB, C, SQL, Python, and R
  • Five or more years of experience in data science and analytical functions
  • experience in the application of advanced statistical methods
  • Experience with the analysis of third party data such as Facebook, Google Analytics, and Adwords.
  • Knowledge of system integration and architecture
  • The ability to present technical terms in a way that is understandable to a non-technical audience.
  • Insight into experimental design and A/B testing
  • Extensive knowledge of predictive modeling algorithms and frameworks


As mentioned earlier, in data science, data engineers and data scientists work together to improve an organization’s business operations. It can therefore be said that the two positions complement each other. Here are the responsibilities you should be aware of when doing this work.

Responsibilities of the Data Engineer :

In general, engineers work with raw data sets that may contain machine, equipment, or human errors. They apply best practices to improve data quality and efficiency. Data engineers are primarily responsible for collecting, designing, creating, and maintaining real-time data. They create the data infrastructure that scientists need. They also support the databases and systems of large companies.

For example, data engineers work in different departments of companies. They, therefore, need better communication skills to effectively explain technical terms to non-technical people. The functions of the computer technicians are as follows

  • Build a data infrastructure to analyze, transform and load data from different sources such as SQL, AWS, etc.
  • Creation of data analysis tools for information channels
  • Interaction with stakeholders from different departments to effectively manage business processes.
  • Identifying, designing, implementing, and improving internal processing
  • Help professionals build data infrastructure and analyze and optimize data.
  • Create manual processes automatically
  • Creating long-term effects of construction solutions and implementing the framework in case of failures
  • Design of data quality frameworks, data integration, and open-source data transfer tools.

Responsibilities of the Data Researcher :

Scientists mainly work with datasets that have been formatted and validated by data engineers. They can therefore effectively implement advanced analytical programs. They also make questions and get answers from the records entering the system. They also search and study hidden data to create better analytical reports. After analyzing the data, the researchers explain these results to the stakeholders on a monthly, weekly, or daily basis. These are the tasks of the data scientists.

  • Understand the needs of businesses and suggest new ideas to improve products using new or old datasets.
  • Presenting the results of the analysis to stakeholders and customers
  • Improvement and support of existing data science products
  • Develop tools to monitor and analyze data infrastructures and maintain data accuracy.
  • Creation of own data models and algorithms
  • Use of appropriate databases and project designs to improve joint development work
  • Develop ideas, create statistical models and conduct experiments.
  • Continue to design data-driven solutions to solve critical business problems.
  • Build a framework for A/B testing and validate the quality of the model.


Engineers and data scientists both earn higher salaries and have a brilliant career ahead of them. This way you can get the best job opportunities based on your skills and experience, regardless of which career you choose.

Data Engineer Salary :

The salary of the computer technicians depends on experience, position, and location. However, the annual salary of a process technician is about 9 lakhs.

The salary of an expert:

Like data engineers, scientists are paid on the basis of their qualifications, skills, experience, and location. The salary of an academic can vary from 7 to 10 lakhs.

In summary:

Now you know the difference between Data Scientist and Data Engineer jobs and can choose the career option that suits you. Whichever career path you choose, you need to have the skills and work effectively to increase your demand.

About the Author: Prateek

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