Start date
Duration (weeks)
2-3 times a week in the evenings
Number of students
Gomel, Belarus
EPAM office in 80 Rechitsky av
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Basic description

Data Engineering is working with data and Big Data: programming of data collecting, storing, processing, searching and visualization.

Required skills

We are waiting for

·         Specialists who are ready to change their profession and start career in IT;

·         Graduates of technical universities without restrictions on distribution after the university;

·         Students of upper grades (technical specialties).

Experience in banking, engineering or any other technical sphere will be a benefit.

Requirements for candidates

·         Basic knowledge of relational Database management system (DBMS) theory;

·         Basic knowledge of SQL (we recommend learning exercises on SQL here);

·         Basic knowledge of network and virtualization technologies (you can get acquainted  VirtualBox);

·         English proficiency level - Pre-Intermediate - Intermediate (A2-B1).
Willingness to level up English skills in a short time;

·         Ability to work in a team, good communication skills, readiness for self-development and learning a big amount of material in a short time.

Training details

We will give you an opportunity to acquire / expand your knowledge of working with data:

·         analysis, forecasting and data integration;

·         building data warehouses in tens of terabytes;

·         data visualization to solve business problems;

·         quality assurance in data storage and management systems.

If you successfully pass the training, you will become a Data Development / Consulting / Testing / Data Analysis Specialist with the opportunity of further employment in EPAM.


Training: 2-3 hours in the evening 2 times a week on weekdays at Rechetskiy, 80 (4 months)

List of recommended literature:

·         К. J. Date. Introduction to Database Systems. — p. 1328. — ISBN 5-8459-0788-8.

·         Ralph Kimball. The data warehouse ETL toolkit - practical techniques for extracting, cleaning, conforming, and delivering data. Wiley, 2004

P.S. To consolidate skills, use materials for self-preparation "Materials for self-studying (for future Data Engineers)"