Data Engineers

The average salary for a data engineer is around €60,400 gross per year.  
The range is between a minimum of €50,500 and a maximum of €83,700 gross per year. 

As with other jobs in the IT sector, the salary increases with experience. For example, the salary of a Data Engineer with the title “Head of Data Engineering” can be over €100,000 gross per year.  
A Senior Data Engineer can earn around €77,000, while a Junior Data Engineer earns just over €53,000. 

The average hourly wage for freelance Data Engineers is €117, which corresponds to a daily rate of €936. The high hourly rate is mainly calculated by omitting various payment factors that exist for permanent employees. These include, for example, insurance, vacation days, but also the hardware that is usually provided to permanent employees. 

Other factors that influence the salary are as follows: 

Salary After Graduation 
After completing a bachelor’s degree, the starting salary is €47,500 on average. The starting salary for data engineers with a master’s degree averages €52,780 and for graduates with a doctorate €62,280. 

Salary by Company Size 
The size of the chosen company also plays a decisive role. In a start-up, with fewer financial possibilities, the average salary is around €49,000. In medium-sized companies, it’s €52,250, and in a large company it’s just over €59,000. 

Salary by Industry 
You earn the most as a data engineer in the automotive industry. Here, the starting salary is between around €53,000 and €62,000. The retail sector pays below average with a starting salary of between €44,000 and €51,000. 

Salary by State  
As with most other IT professions, Hesse tops the list with an average salary after entry of between €53,500 and €62,000. The states of Bavaria, Hamburg, and Baden-Württemberg also pay well. 
Rather less is earned in the newer federal states, such as Saxony-Anhalt. 

Data Engineers are responsible for everything that has to do with the creation and maintenance of data. 

Data Engineers provide the data that Data Science experts need for further analysis. Data Engineers receive large and unstructured data sets from various sources. Through pipelines and scripts, Data Engineers get the data into the right format and location. This process is called extract, transform, load (ETL).  

Despite difficult tasks and complex algorithms, data engineers work to keep the code as simple as possible to allow others to easily understand it. 
Data engineers also take care of setting up and monitoring the IT infrastructure.  

Depending on the company, other tasks may also be added, such as purchasing and setting up hardware components with the appropriate software. 

Simply put, data engineers function as the interface between hardware and data processing. This also includes the security and reliability of the entire system. 

Knowledge of data security and data protection is therefore also welcome. 

A Data Engineer deals with the preparation of data. They develop, design, test and maintain the complete architecture. A Data Engineer has a strong technical background and has the ability to create and integrate APIs. They have a basic understanding of data pipelines and performance improvements. 

The Data Engineer sorts and structures the unstructured data that the Data Scientist needs for further analysis and interpretation. 

Data Scientists analyze and interpret the complex, digital data that the Data Engineer has previously prepared. For this, they need a deep understanding of statistical data analysis, machine learning, etc…. 
In addition, a Data Scientist also needs extensive industry knowledge in order to put the available data into a meaningful context. In the area of machine learning, the role of the data engineer also does not require such in-depth knowledge as that of the data scientist. 

The tasks and roles of Data Scientists and Data Engineers are quite close to each other. The Data Engineer develops, tests and improves architectures, while the Data Scientist is responsible for creating operational models. Data Scientists are the link between stakeholders and customers. 

In smaller teams, it can also happen that a Data Engineer has to take on several roles, such as Data Analyst. 

Basically, data engineers need knowledge of various programming languages. Probably the most common programming language at this point is Python. In addition, there are tools such as Hadoop, Spark, Hive and Kafka. The query language SQL is also an important skill for the data engineer. 

In addition to skills on various tools and programming languages, a data engineer needs knowledge on various data processes. Knowledge from other or related areas is also important in order to be able to take on further tasks in case of doubt.  

Since data engineers work closely with data scientists and other developer positions, good communication skills and the ability to work in a team are also indispensable. 

As a rule, data engineers can present a university degree. Typical courses of study are the branches of computer science or business informatics.  

However, a university degree is not necessarily a prerequisite.  

You still have the option of making a lateral move through further training or work experience in the field of data engineering.  

However, the chances of a higher starting salary increase with the level of the degree.  

Since the profession is still quite young, there are hardly any opportunities for specialization. However, the role of big data engineer or data architect, for example, is possible.