To define the professional figure of the Data Engineer it is useful to refer to Big Data, that is the amount of information that is produced every day from different sources, such as the Internet, sensors, IoT devices, mobile ecosystem, and research centers.
According to estimates made by researchers at Seagate UK, at the end of 2020 the volume of data generated globally was equal to 44 zettabytes, while from 2025 on, no fewer than 463 exabytes will be produced every day worldwide. Considering the forecast that by the end of 2030 90% of people over the age of 6 will be digitally active, it is clear that these values are likely to further increase.
In a context characterized by more than ever available information, Data Driven companies are also multiplying, whose business model and decision-making processes are precisely based on data.
Therefore, it becomes necessary to consult a professional who can collect and prepare data for their analysis, processing and enhancement. These skills characterize the Data Engineer, an “interpreter” who has the ability to understand what is communicated by the data, even in the face of great amounts and levels of complexity.
Data Engineers: Who are they and what do they do?
On a technical level, the work of the Data Engineer consists in creating systems with which to collect, manage and convert raw data into useful information for the development of analysis strategies, the creation of models, the implementation of applications, and Business Analysis. For this reason, it is a figure who often works side by side with programmers, Database Administrators and Data Scientists.
In the context of a small company, a Data Engineer may have to deal with all those tasks relating to the use of data for purposes associated with the improvement of the production processes.
In more structured realities, instead, it is possible that several professionals have the task of managing engineering and specialist aspects, such as data organization and structuring (Data Warehousing), or the management of data workflows (Data Pipeline).
In general, among the tasks performed most frequently by Data Engineers, we find the development of algorithms for raw data processing, the definition of policies and methods for validating the incoming data, the selection of data based on the reference business model, the database design in collaboration with other technical figures and, last but not least, the compliance audits of the systems for data acquisition and conversion with the company specifications on security, and regulations regarding their processing.
How to become a Data Engineer
Data Engineers require multidisciplinary skills ranging from database management to programming, from Business Intelligence to Artificial Intelligence and Machine Learning technologies.
Data Engineers must, therefore, have advanced skills regarding the most common RDBMS (Relational Database Management System) in the business environment, such as MySQL, MariaDB, and PostgreSQL. Likewise, there obviously needs to be a thorough knowledge of non-relational solutions (or NoSQL) and frameworks specially designed for Data Engineering, such as Apache Spark.
As for programming, the languages most used in this work are Python, R, Java, Scala and of course SQL for interacting with relational databases. Languages such as Python and R are also particularly suitable for the development of projects aimed at creating and training Machine Learning models, which have now become fundamental for data processing, management and automated analysis.
Of course, we cannot forget the need to know how to master useful applications for ETL processes (Extract/Transform/Load), that is the procedures that allow you to extract, transform and load data, regardless of their source of origin.
These are generally graduates in Computer Science, Engineering or other data management-related disciplines. Much of their skills are acquired in the field, through a non-stop update. For this reason, companies often choose candidates with a minimum working experience of at least 3 years.
To prepare for a career such as that of the Data Engineer, it is useful to take part in a specialized training course, such as the Business Data Analysis Master offered by Talent Garden, which is reserved for professionals with at least 3 years of work experience who want to learn how to use data to develop innovative business strategies.
Aimed at professionals such as Product Managers, analysts, marketing managers and Sales Managers, the next edition of the master course will take place from 6th May to 9th July 2022, both online (4 weekends of live learning sessions) and in person lessons (2 weekends) at the Calabiana Campus of Talent Garden, located in Milan.
The salary of a Data Engineer
The annual salary of a Data Engineer in Italy is around 29,000 euro per year for the entry level position, but it can reach just under 55,000 euro a year, in the case of a professional with greater experience.
On average, the annual salary should therefore be equal to about 40,000 euro, that is, over 3,300 euro gross per month. This means that among IT professions, the earnings of the Data Engineer are generally only slightly lower than those of Senior Engineers, but higher compared to those of many other highly-qualified professionals, such as the iOS application developer, the process engineer, and the programmer.
Expected wages may vary considerably depending on the target market, and earning opportunities increase if we take into account international businesses’ demand for labor. In the United States, for example, a Data Engineer has an average salary of 112,000 dollars per year.
The difference between Data Engineer and Data Scientist
The figure of the Data Engineer is often confused with that of the Data Scientist who, however, performs distinctly different tasks. From the point of view of the Data Pipeline, Data Engineers act in the initial stages of the workflow, as they collect, select and validate raw data, and then prepare them for the subsequent stages.
Once Data Engineers enhance all data by improving the quality, these become available in a more refined shape for Data Scientists who analyze them with the purpose of obtaining information relevant to decision-making processes, trends and statistics (insights).
Data Engineers are therefore those who “activate” data, or rather make them usable, by converting them into a useful format to simplify the work of Data Scientists, and to maximize the productivity resulting from their analysis.
The increase of Big Data and their rising importance in planning corporate strategies, as well as in decision making processes, make the functioning of businesses more and more related to data, often irrespective of their size.
To manage this apparently unmanageable information flow, a highly specialized figure such as that of the Data Engineer is required, a professional able to collect and enhance raw data by converting them into a format that can be used for analysis.