Monday 14 September 2020

Who to work in IT in 2020 - a list of promising professions

If you want to build a career in IT, there is no better time than now. According to Yandex, the share of IT vacancies among all job offers is  steadily increasing from year to year , and the market is favorable to newcomers - since 2016, the volume of entry-level vacancies has grown by almost a third. Where to go to build a successful career? 

Dmitry Pomogaev, author-journalist of  SkillFactory , found out which specialties are in the greatest demand among companies, and made the top 9 most in-demand professions in IT. 

The rankings were used by  the study  of "Yandex"  rating  on Glassdoor and CNBS and  research  job market one of the largest HR-agency Robert Half. Most of the vacancies on the list require a technical background, but there are also those that are suitable for absolutely "zero" specialists. Go.

Data scientist

It is impossible to start the ranking of hot IT specialties in any other way. Moreover, it is in this area that newcomers are most actively sought. A data science expert  brings together  an analyst, a machine learning specialist, and a visionary who discovers hidden opportunities in data. 

In addition to a powerful technical apparatus, he  needs  advanced communication skills - the success of a data scientist depends on how he receives initial information from colleagues and presents the results to them. 

Data Science is  attended by  professionals from statistics, analytics and other fields related to serious data analysis. You can also move into this profession from data analysts who are less involved in coding and math and more immersed in organizing information warehouses.

Business analyst

If you have experience in working with databases and analytical systems, the road to the analytical departments of companies is open for you. 

Such vacancies are now being opened by a variety of organizations, because the lazy is already talking about the benefits of data for business. So the analyst can choose the industry he is interested in and change the occupation within his company at will - these skills can be used in strategic management, and in production areas, and in marketing.

To start a career as a business analyst, you will need to become proficient in Excel and database query languages, OLAP and multidimensional cubes. 

Cloud Infrastructure Expert

Another area that has been gaining  popularity for several years  . A beginner can apply for a cloud engineer position where they will design, implement, and maintain virtual data infrastructures. You can't do without working with Amazon Web Services, OpenStack and Microsoft Azure, cloud databases and APIs.

An experienced professional can evolve horizontally or rise to a cloud architect who drives strategic development of enterprise virtualized solutions. Accordingly, the practical skills need to add familiarity with ITSM, the principles of data management, process automation in multi-vendor architectures.

Data Engineer

Data Engineer or data engineer is a person who is responsible for data infrastructure, retrieval, storage and processing. 

Data Engineer can easily: Computer questions

understand the essence of the request and collect the necessary data;

build a data pipeline architecture;

transform the model obtained by the researcher into a finished product. 

As you can see from the duties of a data engineer, his role in the company is especially important - he participates in the initial and final stages of data analysis, and also supports the created pipelines. 

Working in tandem with Data Scientist, the data engineer dramatically increases the efficiency of his work. To do this, a professional data engineer needs to have strong programming skills to write quality, clean code, as well as a deep understanding of the big data ecosystem and distributed systems. 

In short, his skills are Hadoop, MapReduce, Hive, Pig, data streaming, NoSQL and SQL. At the same time, he does not need to have fundamental knowledge in mathematics and skills in building algorithms, unlike a data scientist.

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