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.
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. To do this, one cannot do without knowledge of statistics and the basic mathematics associated with statistics.
Machine learning and data analysis tools are indispensable in today's world. If familiar 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 scientist must effectively communicate the results of the analysis. This will help him visualize the data or the results of studies and testing 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:
Databases and data warehouses, Business Intelligence:
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:
Machine learning will be used everywhere, and it is important to understand what business problems it will help 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.
In international practice, the starting 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% annual bonuses.
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 practically the same. Usually consists of five stages.
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.
It usually takes place over the phone. Consists of one or two difficult and as many simple questions regarding the current employer stack.
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.
Data Scientist and Data Engineer are both strategic and new positions for many companies. It is important that a potential colleague like the leader and coincide with his views.
What will help data scientists and data engineers in 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 minimal background and knowledge of the basics of popular tools can gain the necessary experience if they learn and develop on their own.
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's 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 how to think in 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 it is important for them to know about data, as well as how buyers interact with applications: job search and dating, car rental, podcast application, educational platform.
Analyst, Data Scientist and Data Engineer positions 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.