Data engineering is an area that is still underestimated by many companies when it comes to turning their data into added value. In this blog article, you will learn why the data engineer is a key position in data science teams, as well as all the essentials about the job description and training opportunities.
More and more companies are deciding to use data analytics in their organisation. One of the first thoughts when Recruiting is usually to bring a data scientist into the company. To be successful Data projects but several roles must be filled in one team (Data Roles).
A data science team combines numerous skills and professions. Besides the Data Scientist takes the Data Engineer plays a key role in this. It is the guarantor for reliability and performance of the IT infrastructure.
Data engineering is a comparatively young phenomenon. The data engineer - sometimes also Big Data Engineer or Big Data Architect is perhaps best described in German as "data engineer". Although there is almost no opportunity to study data engineering as a classical degree course, the job description will become increasingly important in the future.
What does a data engineer do?
The data engineer takes care of all processes related to the generation, storage, maintenance, preparation, enrichment and dissemination of data. An important task here is, for example, the Setting up and monitoring the hardware and software infrastructure. This includes conception, purchasing as well as setting up all necessary components that are appropriate for the respective requirements.
The decisions as to which Software and which Services The data engineer makes, implements and monitors the decisions that are taken in order to carry out the analyses. For example, he must be able to set up and maintain a database such as MongoDB or databases in SQL.
Besides the Management and monitoring of data and data sources the data engineer is the interface between the data and the entities responsible for analysis and further use. In this context, he or she is not only responsible for the selection of the correct Data sets responsible, but also optimises Algorithms or takes Productivity tools in operation that make it easier for staff to handle the data.
Last but not least, the data engineer also takes care of the Security and Stability of the entire system and compliance with data protection and Data security.
Reading Tip: No data lake can reasonably be operated without data engineering - read more here about the basics and everything you need to know about the Data Lake.
What skills does a data engineer need?
One of the critical skills of a data engineer is to, Know all the requirements of data processes and scale data volumes to be able to do this. Many companies underestimate the high capacities that are sometimes necessary, especially when it comes to machine data in the context of Industry 4.0. The use of Cloud services represents a possible solution here, because here, if required, the Storage capacity can be increased slightly.
Especially in small companies where only one data engineer or only a small team is responsible for these tasks, a data engineer must be a good Allrounder be. In larger companies, however, the individual tasks become so time-consuming and sometimes so complex that it is no longer possible for one person to take on everything equally.
A data engineer should have advanced knowledge in the Programming have. It is also advantageous to have basic knowledge of Algorithm development. On the one hand, software and algorithms may need to be extended by individual, customised components. On the other hand, data engineers work directly with data scientists and this collaboration is greatly facilitated when a Basic understanding of the following work step is in place. Data science skills also help to build up a suitable infrastructure that is sufficient in the long term.
Often underestimated is also the communicative and interpersonal aspect. Every day, data engineers are in contact with people whose professional expertise may come from a completely different field. Responses from data engineers are usually very technical, due to the complex hardware and software used in Data analysis processes are involved.
At the same time, their decisions influence the everyday work of these colleagues. For success, it is often important to find problems and solutions in simple words to be able to explain their decisions so that other team members and employees in the company understand them.
The traditional engineer is considered Problem solverwho above all Work well under pressure must be able to do. The same applies to the data engineer. In times when almost all companies depend on their IT infrastructure, it is crucial to get the system up and running again as quickly as possible in the event of a breakdown. It is often the case that one data engineer alone in a company is responsible for ensuring that the system environment runs cleanly.
Even if they work in a team, it is rather rare that they find other experts elsewhere in the company whose advice they can draw on. The ability, Independent solutions is therefore also one of the core competencies.
How does one become a data engineer?
In most cases, data engineers come from the fields of Computer Science, Business Informatics and Computer technology. However, this does not preclude someone with a basic education in statistics, who at the same time has initial experience in the field of engineering, from specialising in data engineering later on.
In addition to personal preferences, this decision also depends heavily on the particular company in which someone wants to make a career, or on the specific Data science projects - in short: Learning on the job. The framework conditions therefore strongly determine which specialisation or which exact knowledge is relevant and must be learned.
What potential does the profession of data engineer have?
In the course of the increasingly comprehensive Digitisation the profession of data engineer is also becoming increasingly important. Almost every company will soon have more or less complex IT infrastructures and the need for data analysis or data management. According to a forecast by the Market research institute Ovum develop Data Science and data engineering the key success factors.
The demand will therefore continue to increase very strongly, especially in the IoT environment. In any case, the demand for data engineers will continue to rise sharply in the future. In addition to the data scientist, the Data Engineer one of the most attractive professions of the 21st century.