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Data Engineer and Data Scientist: what they can and how much they earn

Together with Elena Gerasimova, head of the Department of Data Science and Analytics in Netology, we continue to understand how Data Scientist and Data Engineer interact with each other and how they differ.

In the first part, we talked about the main differences between Data Scientist and Data Engineer .

In this article, we’ll talk about what knowledge and skills specialists should have, what kind of education is appreciated by employers, how interviews are held, and how much data engineers and data scientists earn. 

What Scientists and Engineers Should Know


Profile education for both specialists - Computer Science.



Any data specialist - a data scientist or analyst - must be able to prove the correctness of his conclusions. To do this, one cannot do without knowledge of statistics and statistics related to basic mathematics .

Machine learning and data analysis tools are indispensable in the modern world. If the usual tools are not available, you need to have the skills to quickly learn new tools, create simple scripts to automate tasks .

It is important to note that the data specialist must effectively communicate the results of the analysis. Data visualization will help him in this .or the results of research and hypothesis testing. Specialists should be able to create charts and graphs, use visualization tools, understand and explain data from dashboards.



For a data engineer, three areas come to the fore.

Algorithms and data structures . It is important to get your hand in writing code and using basic structures and algorithms:

  • complexity analysis of algorithms,
  • ability to write clear, supported code, 
  • batch processing
  • real time processing.

Databases and data warehouses, Business Intelligence :

  • data storage and processing,
  • holistic systems design
  • Data Ingestion,
  • distributed file systems.

Hadoop and Big Data . There is more and more data, and on the horizon of 3-5 years, these technologies will become necessary for every engineer. A plus:

  • Data Lakes,
  • work with cloud providers.

Machine learning will be used everywhere, and it is important to understand what business tasks it will help to solve. It is not necessary to be able to make models (data Scientists will cope with this), but you need to understand their application and relevant requirements.

How much do engineers and Scientists get


Revenue Data Engineers


In international practice, the initial salary is usually $ 100,000 per year and increases significantly with experience, according to Glassdoor. In addition, companies often provide stock options and 5–15% per annum bonuses.

In Russia, at the beginning of a career, salaries are usually not less than 50 thousand rubles in the regions and 80 thousand in Moscow. At this stage, experience is not required, except for the training passed.

After 1‒2 years of work - a fork of 90‒100 thousand rubles.

The fork increases to 120-160 thousand in 2-5 years. Factors are added, such as the specialization of past companies, the size of projects, work with big data, and more.

After 5 years of work, it is easier to look for vacancies in related departments or respond to such highly specialized positions as:

  • An architect or leading developer at a bank or telecom - about 250 thousand

  • Pre-Sales by the vendor with whose technologies you worked most tightly - 200 thousand plus a bonus (1-1.5 million rubles) is possible. 

  • Experts in implementing Enterprise business application, such as SAP, - up to 350 thousand

Data Scientist Income


Investigation of market analysts of "normal research" and recruiting agency New.HR shows that specialists Data Science earn an average salary greater than analysts had other specialties. 

In Russia, the initial salary of a data scientist with work experience of up to a year is from 113 thousand rubles. 
As an experience, the passage of training programs is now also taken into account.
In 1-2 years, such a specialist can already receive up to 160 thousand.

For an employee with experience of 4-5 years, the fork grows to 310 thousand.

How are the interviews


In the West, graduates of vocational training programs undergo their first interview on average 5 weeks after graduation. About 85% find work after 3 months.

The process of interviewing vacancies for a data engineer and a data scientist is practically the same. Usually consists of five stages.

Summary . Candidates with non-core previous experience (for example, from marketing) need to prepare a detailed cover letter for each company or have recommendations from a representative of this company.

Technical screening . It usually takes place by telephone. It consists of one or two complex and as many simple questions regarding the current stack of the employer.

HR interview. Can pass by phone. At this stage, the candidate is checked for overall adequacy and ability to communicate.

Technical interview . Most often takes place in person. In different companies, the level of positions in the staffing table is different, and positions can be called in different ways. Therefore, at this stage, it is precisely technical knowledge that is checked.

Interview with the technical director / chief architect . Engineer and Scientist are strategic positions, and for many companies, they are also new. It is important that the potential colleague likes the leader and matches his views.

What will help Scientists and engineers in their career growth?


There are a lot of new tools for working with data. And few people are equally well versed in all. 

Many companies are not ready to hire employees without work experience. However, candidates with a minimum base and knowledge of the basics of popular tools can get the necessary experience if they are trained and developed independently.

Useful qualities for a data engineer and data scientist


Desire and ability to learn . It is not necessary to immediately pursue experience or change work for the sake of a new tool, but you need to be prepared to switch to a new area.

The desire for automation of routine processes . This is important not only for productivity, but also to maintain high quality data and speed of its delivery to the consumer.

Attentiveness and understanding of “what's under the hood” of the processes . The specialist who has the history and thorough knowledge of the processes will solve the problem faster.

In addition to excellent knowledge of algorithms, data structures and pipelines, you need to learn how to think in products - to see architecture and business solution as a single picture. 

For example, it is useful to take any known service and come up with a database for it. Then think about how to develop ETLs and DWs that fill it with data, what consumers will be and what it is important for them to know about the data, as well as how customers interact with applications: for job search and dating, car rental, podcast application, educational platform.
The positions of the analyst, data scientist and engineer are very close, so you can move from one direction to another faster than from other areas.
In any case, the owners of any IT background will be easier than those who do not. On average, motivated adults retrain and change jobs every 1.5–2 years. This is easier for those who study in a group and with a mentor, compared with those who rely only on open sources.

From the editors of Netology


If you look closely at the profession of Data Engineer or Data Scientist, we invite you to study the programs of our courses:


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