Together with Elena Gerasimova, head of the faculty "»In Netology, we continue to understand how they interact with each other and how the Data Scientist and Data Engineer differ.
In the first part they told .
In this material, we will talk about what knowledge and skills specialists should have, what kind of education is valued by employers, how interviews go, and how much data engineers and data scientists earn.
What Scientists and Engineers Should Know
The profile education for both specialists is Computer Science.
Any data scientist - data scientist or analyst - should be able to prove the correctness of their conclusions. This requires knowledge statistics and statistics-related basic mathematics.
Machine learning and data analysis tools are indispensable in today's world. If the usual tools are not available, you need to have the skills quickly learning new tools, creating simple scripts to automate tasks.
It is important to note that the data scientist must effectively communicate the results of the analysis. This will help him data visualization or the results of research and testing of hypotheses. Professionals 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 the basic structures and algorithms:
- algorithm complexity analysis,
- ability to write understandable, maintainable code,
- batch processing,
- real time processing.
Databases and data warehouses, Business Intelligence:
- storage and processing of data,
- design of complete systems,
- 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. Plus:
- data lakes,
- work with cloud providers.
Machine learning will be used everywhere, and it is important to understand what business problems it will help to solve. It is not necessary to be able to make models (data scientists can handle this), but you need to understand their application and the corresponding requirements.
How much do engineers and scientists earn
data engineers income
In international practice The starting salary is typically $100 a year and increases significantly with experience, according to Glassdoor. In addition, companies often provide stock options and 000-5% annual bonuses.
In Russia at the beginning of a career, the salary is usually not less than 50 thousand rubles in the regions and 80 thousand in Moscow. At this stage, no experience is required other than the completed training.
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 such as the specialization of past companies, the size of projects, work with big data, and so on are added.
After 5 years of work, it is easier to search for vacancies in related departments or respond to highly specialized positions such as:
- Architect or lead developer in a bank or telecom - about 250 thousand rubles.
- Pre-Sales from the vendor whose technologies you worked with the most closely - 200 thousand plus a bonus (1-1,5 million rubles) is possible.
- Enterprise business application implementation experts, such as SAP, up to 350
Data scientist income
of the market of analysts of the company "Normal Research" and the recruiting agency New.HR shows that data scientists receive on average a higher salary than analysts in other specialties.
In Russia, the starting salary of a data scientist with up to a year of experience is from 113 rubles.
Work experience is now also taken into account the passage of training programs.
After 1–2 years, such a specialist can already receive up to 160 thousand rubles.
For an employee with 4-5 years of experience, the fork grows to 310 rubles.
How are the interviews
In the West, graduates of vocational training programs have their first interview an average of 5 weeks after graduation. About 85% find a job after 3 months.
The process of passing interviews for the vacancy of a data engineer and a data scientist is almost 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 over the phone. Consists of one or two difficult and as many simple questions regarding the current employer stack.
HR interview. Can be done by phone. At this stage, the candidate is tested for general adequacy and ability to communicate.
technical interview. Most often passes internally. In different companies, the level of positions in the staffing table is different, and positions can be called differently. Therefore, it is technical knowledge that is tested at this stage.
Interview with CTO/Chief Architect. Engineer and scientist are strategic positions, and new for many companies. It is important that a potential colleague like the leader and coincide with his views.
What will help scientists and engineers in their careers
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 minimal background and knowledge of the basics of popular tools can gain the necessary experience if they learn and develop on their own.
Useful Qualities for a Data Engineer and Data Scientist
Willingness and ability to learn. You don't have to jump right into experience or change jobs for a new tool, but you do need to be ready to switch to a new field.
The desire to automate routine processes. This is important not only for productivity, but also for maintaining the high quality of data and the speed of its delivery to the consumer.
Attentiveness and understanding of “what is under the hood” of processes. The specialist who has a good eye and a 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 to think products — to see the architecture and business solution as a single picture.
For example, it is useful to take any well-known service and come up with a database for it. Then think about how to develop ETL and DW that will populate it with data, what consumers will be and what they need to know about the data, as well as how buyers interact with applications: job search and dating, car rental, podcast application, educational platform.
The positions of an analyst, a data scientist, and an engineer are very close, so you can move from one direction to another faster than from other areas.
In any case, it will be easier for owners of any IT background than for those who do not have it. On average, motivated adults retrain and change jobs every 1,5–2 years. It is easier for those who study in a group and with a mentor, compared to those who rely only on open sources.
From the editors of Netology
If you are looking at the profession of Data Engineer or Data Scientist, we invite you to study the programs of our courses:
- Profession "».
- Profession "».
- Well "».
Source: habr.com