Hello, Habr! On September 28th Skillfactory is launching a new Data Analyst course stream , so we decided to do a broad overview of the job market that companies are offering today.
Can the profession of data analyst really generate up to 300k / nanosec? What skills do employers require from analysts and what do you need to know in general to become a sought-after and highly paid specialist? What growth opportunities does the market offer today?
We analyzed 450 vacancies for the position of data analyst in Russia and abroad and collected the results in this article.
Who is a data analyst and what he needs to know
Before analyzing vacancies, let's look at what Data Analyst does in a company. In the IT field, there are three areas of specialization in working with data: Data Analyst, Data Engineer and Data Scientist.
Data Analyst collects information, processes and interprets it into "human language". In fact, it translates statistics and big data into understandable and visual conclusions that can be used to develop a specific project or business in general.
The result of the data analyst's work is the basis for making any business decisions.
Data Engineerworks no longer with the data itself, but with their infrastructure: databases, storage and processing systems. The data engineer determines how to analyze the data so that it is useful to the project. To summarize, the Data Engineer is setting up a data processing pipeline.
The Data Scientist deals with strategic information work. It is he who creates forecasting, modeling and dynamic analysis systems, implements automation and learning algorithms.
The main difficulty is that the boundaries between these three specialties are rather blurred. Most companies do not see the difference, so there are often requirements in Data Analyst jobs that are more suitable for Data Engineers or Data Scientists.
This is mainly due to the specifics of the market. If IT companies know that Data Analyst, Data Engineer and Data Scientist are ideally three different specialists or even three different departments, then in product companies and industries they often don't even think about it.
What employers want from a data analyst
We analyzed over 450 vacancies for a data analyst position opened in August-September 2020. In many cases, the requirements for specialists are very different. As we wrote above, the boundaries between Data Analyst, Data Engineer and Data Scientist have been erased, so it often happens that the title of a vacancy is written “Data Analyst”, but in fact the vacancy fully corresponds to “Data Engineer”. But we were able to highlight the set of hard and soft skills that employers indicate in most vacancies for the position of data analyst.
Hard skills
Python with Pandas and NumPy data analysis libraries . This is a must-have, its knowledge at least at a basic level is required by 83% of companies in the industry. Only 17% of employers need knowledge of R, JavaScript and other programming languages.
Interestingly, in 2013, according to a survey of data analysts and data scientists, the R language was much more popular in data analytics - it was used by 61% of specialists.
SQL - Almost all jobs require knowledge of SQL and relational database skills. Most often, they require the ability to write queries and optimize them.
Employers rarely require skills in NoSQL database management systems like MongoDB, CouchDB or Apache Cassandra - about 9% of vacancies.
Power BI, Qlik, Tableau . Most companies do not require knowledge of any particular data visualization program. Usually they indicate one of three to choose from or write "data visualization systems" without specifying a specific one. In general, specialists can choose what is more convenient for them to use. The absolute majority of employers do not have a principled position.
Experience with Agile, Scrum, Kanban... In almost half of the vacancies, employers indicate that an additional advantage will be the ability to work with agile product creation methodologies.
That is, it is important not only what the data analyst does within his specialty, but also how he does it.
But experience with Agile is not a key requirement (although it is indicated in vacancies). Yes, the job seeker will have to take time to get used to working in this format, but, according to companies, this is not critical.
Excel and Google Sheets . Oddly enough, but a third of vacancies require knowledge of spreadsheets. This is mainly needed by product and consulting companies that have little overlap with digital development, or relatively small projects where the entire analytics department consists of several people.
Indeed, small teams often do not need to use powerful SQL resources if regular Excel is enough to process the data. But in such situations, the "data analyst" often does everything at once: collecting and analyzing data, infrastructure and automation.
Many companies highlight a high level of mathematical background . But here you need to understand that Data Analyst, unlike Data Scientist, uses rather limited mathematical tools, so you don't need to be a math genius. Most of the tasks of a data analyst fit into the framework of basic knowledge of statistics, probability theory, mathematical analysis and linear algebra.
A higher education in mathematics is useful, but with due diligence, you can learn all the necessary functions yourself. But for the Data Scientist, a deep knowledge of mathematics is already considered critical. If you plan to grow from Data Analyst to Data Scientist, then the math will need to be tightened.
For basic hard skills, that's all. The rest are found in less than 10% of vacancies, so they can be attributed to the individual characteristics of work in individual companies.
Soft skills
In general, they are practically the same for all specialties that work with data:
- Critical thinking
- Analytic mind
- Ability to correctly express and convey information
- Responsibility and attention to detail
- Business thinking
- Willingness to make decisions and take responsibility for the result
- Multitasking
- Sense of humor
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Only the English language stands apart from the soft skills . Many companies mark knowledge of English as an advantage, but there are a number of vacancies that are designed to work in international teams and with English-language projects. In such, fluency in English is a must.
Compulsory English is often a pleasant paycheck. Vacancies in international projects guarantee monetary compensation 1.3-2 times more than in Russian-speaking ones.
Salary and other goodies for data analysts
Now let's move on to the fun part - the salary. We analyzed the open vacancies on the HH.ru and Habr Career websites .
Data analysts are in demand in any large and medium business, especially in those projects that relate to digital and IT. Fintech banks, digital agencies, food companies that establish an online sales system, consulting projects. Among the vacancies there are representatives of business in almost all spheres: from medicine to heavy industry.
Most vacancies for data analysts as of 09/12/2020 are open in Moscow (241) and in St. Petersburg (74). For comparison, there are only 99 vacancies for this position in the rest of Russia.
Interestingly, only 20% of companies indicate the salary level in the ad itself. The remaining 80% prefer to discuss monetary rewards in a personal conversation with the applicant.
The spread of salaries is quite large. It depends not only on the experience of the applicant, but also on the geography. For example, a trainee analyst in Perm receives 25,000 rubles, while Data Analyst in the Moscow office of an international company earns 200,000 rubles.
In Moscow, the average salary for a data analyst is 134,000 rubles. A good specialist with at least 2 years of experience can well count on her.
In St. Petersburg, the situation is similar to that in Moscow, but salaries are slightly lower. The average data analyst can count on 101,000 rubles a month. As for the rest, the conditions are almost identical to those in Moscow.
Trainees and Junior specialists receive from 60,000 rubles. There are a small number of vacancies that offer below this amount (8%), but they mostly offer part-time or limited weekly work.
Heads of analytics departments and senior specialists can count on a salary of 170,000 rubles or more. There are even vacancies that offer more than 250,000 rubles a month. Yes, they require more than 5 years of experience in analytics and a large pool of competencies, but there are such vacancies. So it's pretty clear where you can grow.
Additional benefits and motivators are often cited as opportunities for corporate training, health insurance, and even corporate retirement programs. Some companies offer relocation to Europe or the USA after a certain number of years with the company. Beloved by many "cookies and coffee" are also found, but quite rarely. Most employers rely on really useful motivators.
In other cities of Russia, the situation is worse. They partially erase the very essence of the data analyst's work, he becomes more like an enikeys. In small companies for several dozen people, the analyst is generally one and completely processes all business information.
The salary of such a specialist is also not top-end. On average, an analyst outside of Moscow and St. Petersburg receives 54,000 rubles. In half of the cases, there are often no additional "buns" at all, but otherwise they are limited to bes̶p̶l̶a̶t̶n̶y̶m̶ ̶k̶i̶p̶ya̶t̶o̶ch̶k̶o̶m̶ ̶n̶a̶ ̶k̶o̶f̶e̶p̶o̶y̶n̶te̶e̶ “sportsmen and coffee.
The maximum salary of a data analyst that a specialist in the regions can count on is 100,000 rubles. But to get more, you don't have to move to Moscow. You can easily find remote vacancies - formally work in the capital, but live in your hometown. Many companies go to meet the applicant in whom they are interested.
We also conducted a comparative analysis of vacancies from Ukraine and Belarus.
The average salary of a data analyst in Ukraine is about UAH 20,000 (RUB 53,000). In the capital, there are vacancies with pay 2-2.5 times higher, but they are offered mainly by international companies with branches in Kiev.
The situation is absolutely the same in Belarus. The average salary of a data analyst is 2,800 Belarusian rubles (81,000 rubles), but the range of salaries is very large. In Gomel, for example, an analyst with a year's experience earns an average of 1100 Belarusian rubles (31,000 Russian rubles), while in Minsk a specialist can earn up to 10,000 (287,000 Russian rubles).
Where to come to the profession and where to grow data analytics
It is believed that it is possible to get into the caste of analysts only with exceptional knowledge of mathematics. But this is not the case.
Analytics are usually occupied by Junior and Middle Python developers. If you also have a basic knowledge of SQL, it's generally excellent. In this case, it will be much easier to deal with all the features of the work.
You can also start your career directly with an analyst. Choose from dozens of courses available - and off you go. It is not necessary to know higher mathematics. For Data Analyst Junior and Middle levels, you only need knowledge of tools for working with data. And in most cases, school knowledge of mathematics is enough.
There are plenty of growth opportunities for the data analyst too. The three most obvious ones are Data Mining Specialist, Data Engineer, Data Scientist. The former works directly with finding data for analytics, the latter develops data infrastructures, and the third deals with forecasting and strategy.
Another possible option is BI analytics. Analytics data visualization is a separate skill, and many large companies value employees who can not only analyze information, but also communicate intelligibly to management.
Especially for this material, we asked Alexander Tsarev, the founder of SmartDataLab, the leader of the BI SkillFactory educational course, and Sergey Zemskov, the head of the Power BI / DWH SmartDataLab direction, the Bootcamp SkillFactory teacher to comment on the necessary skills for growth in BI analytics.
The overview lists must-have competencies, but if you want to continue to grow as a Data Analyst, you will need to stay up to date with ETL and learn:
- Microsoft's so-called golden triangle: SSRS, SSIS, SSAS;
- Have an understanding of other industrial ETLs such as KNIME;
- Data architecture literature such as Bill Inmon's Kimball Methodology;
- You also need to understand at least a first approximation what Informatica, GreenPlum, Pentaho are, how they differ from each other and how they work.
- , SAP Web Analytics BI SAP, Power BI (, - BI/DWH “BI HeadHunter”, ).
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Also, a data analyst can grow into a product, marketing analyst or business analyst. That is, to take responsibility for the development of a specific product or project, or to take part in making strategic business decisions, backing up your opinion with analytical data.
Also, data analysts can go completely into development in Python, but this option is chosen by a relatively small number of specialists.
Data analyst is a promising and in-demand profession. And to become a Data Analyst, you don't need to be Perelman and be able to solve Poincaré's theorem - school knowledge of mathematics and perseverance in mastering the analyst's tools are enough.
Recently we launched the first in Russia Online Bootcamp for Data Analyticswhich includes 5 weeks of study, 5 projects in portfolio, paid internship for the best graduate. This is a super-intensive format for the most motivated: you need to study full time.
Find out the details of how to get a high-profile profession from scratch or Level Up in skills and salary by taking online SkillFactory courses:
- Data Analytics Online Bootcamp (5 weeks)
- Data Analytics Course (6 months)
- Analytics profession with any starting level (18 months)
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