Successful data science projects are usually based on teamwork. Companies that start with data analysis often first think of the Data Scientist when recruiting. However, data projects need several roles in a team to be successful. Data science teams combine numerous skills and professions. In addition to the Data Scientist and the Data Artist, the Data Engineer also plays a key role. Data Engineering guarantees the reliability and required performance of the IT infrastructure.
How to become a Data Engineer?
Data engineers are often recruited from areas such as computer science, business information technology and computer technology. However, this does not rule out the possibility that someone with a basic statistical education, who also has initial experience in the field of engineering, will later specialize in data engineering. Personal preferences therefore play an important role. But the framework conditions must also be right. Companies often have a need for a data engineer when it comes to carrying out concrete data science projects. Learning on the job is often an ideal starting point for a career and determines the professional orientation of data engineers.
The Tasks of a Data Engineer
The tasks of the data engineer are manifold. Generally speaking, the Data Engineer takes care of all processes related to the generation, storage, maintenance, processing, enrichment and transfer of data. An important aspect is the setup and monitoring of the hardware and software infrastructure. Starting with the conception, purchase and installation of all necessary components up to the decision which software and which services to use.
Many of the Data Engineer’s activities are located at the interface between hardware and data management or data processing. This includes monitoring of data sources as well as managing the instances that are responsible for analysis and reuse. Therefore, he is not only responsible for selecting the right data sets, but also optimizes algorithms or puts productivity tools for data analysis into operation. An important part of his work is also the security and stability of the entire system. This also includes important aspects such as data protection and data security.
Essential knowledge and know-how
The data engineer must be familiar with all the requirements of a data process and be able to scale data volumes. Companies that are still at the beginning of their Data Journey underestimate the sometimes necessary capacities to store the resulting data. Especially in the context of industry 4.0, i. e. when machine data is involved, it is not uncommon for petabytes of data to accumulate. One solution to the problem of scalability are cloud services, because they can easily increase storage capacity requirements. It is not uncommon for small companies to have only one single data engineer responsible for these tasks. This means that a data engineer must be a good all-rounder. In larger companies, however, the individual tasks become so complicated and sometimes so complex that it is no longer possible for one person to take over everything in equal measure.
The DataEngineer – the “egg-laying wool milk sow”?
In addition to the core competencies mentioned above, a data engineer should also have advanced knowledge in programming. It can happen again and again that algorithms have to be adapted or further developed. The programming skills also make the cooperation between Data Engineers and Data Scientists much easier. Last but not least, knowledge of data science helps to build custom-fit IT infrastructures that are adequate in the long term.
Similar to the job description of the Data Scientist, data engineers often underestimate the importance of the communicative and interpersonal aspect at work. Every day, a data engineer comes into contact with people who come from a completely different professional field. The answers given by a data engineer should not be too technical, so that they can no longer be understood by outsiders. This is all the more important because the decisions of the data engineer can have a strong influence on the everyday work of these colleagues.
The Data Engineer as a problem solver
The communicative aspect is also useful to the data engineer in his role as a problem solver. In times when almost all processes within a company depend on the IT infrastructure, it is crucial to get the system back up and running as quickly as possible in the event of a malfunction. The Data Engineer is often the central point of contact for problems of this kind. As a rule, even if the data engineer is part of a larger team, there are hardly any other experts in the company who could be asked for advice in extreme emergencies. That is why the ability to find solutions independently is extremely important.
A profession with potential
Data engineering is a relatively recent phenomenon. The Data Engineer, sometimes also known as “Big Data Engineer” or “Big Data Architect“, is perhaps the best known in German as “Dateningenieur”. Up to now, there is almost no possibility of attending data engineering as a classical degree course. However, it is already clear that the job description will become increasingly important in the future and has been underestimated by many companies for far too long.
In the course of the increasingly comprehensive digitization process, no company that relies on data analysis will soon be able to do without data engineers. The job also has enormous potential for the increasingly complex IT infrastructures and the increasing amount of work involved in data management. Particularly in companies operating in the IoT and Industry 4.0 environment, demand will continue to rise for the foreseeable future.