The topic of “Big Data” is no longer a new phenomenon, because we are all confronted with it every day and for the most part unconsciously. On social media, when shopping online, or when we talk to Siri on our smartphone. All this data about our behavior is collected, analyzed and evaluated by companies in order to better understand us as customers and gain insight into our buying behavior. However, the term “big data” refers to data sets that are so large, fast-moving or complex that they are difficult to process using conventional methods. To ensure that this is nevertheless possible and that all the data can be analyzed, companies need to get to grips with data science and work together with IT specialists.

The connection between Data Science and Data Engineering

In the modern world, there is hardly an industry that has not yet been revolutionized by Data Science. So-called “Data Scientists” are an important part of large companies. They are responsible for creating a structured database from unstructured raw data, analyzing it and in the end creating a basis for decision-making for a company. This can be helpful to evaluate data about customers and processes and to find out what works and what does not. But, without an architecture that can structure and format growing and changing data sets, Data Scientists are not able to make correct predictions. This is where data engineering comes in.

And what exactly does “data engineering” mean?

Data Engineering is the process of capturing, translating and validating data for subsequent analysis by Data Scientists. Data Engineering, then, lays the foundation for the application of Data Science in the real world. Data Engineers and Data Scientists working harmoniously together can provide ongoing valuable insights for businesses.

Here’s what the work of a Data Engineer looks like

As a Data Engineer, you’re essentially responsible for merging data. This means that you create a landscape from the available data and technologies in which the Data Scientist can thrive. Depending on the company, your exact duties in a data engineering job can vary greatly. This is because data comes into the company in many ways – for example, via user accounts, order transactions, or even interaction in social media. Here, no matter how much data is generated, you are able to scale and structure it. This is usually done in the data warehouse. For storage, you then use either frameworks such as Hadoop or cloud services such as AWS. So you should not only know about the data available in the company and where it is stored, but also how best to integrate this data into a central analysis infrastructure, which technologies are suitable for this and which additional external data is used for enrichment. Last but not least, as a data engineer you are also responsible for ensuring that the system you have developed functions properly. To do this, you monitor and make changes to your algorithms to make them even better.

Career opportunities and income as a Data Engineer

Your starting salary as a junior data engineer is between €40,000 and €55,000. In a large company you will tend to earn a little more, in a startup a little less. However, you usually have more freedom and work in a relaxed atmosphere. After 2-3 years you will have the opportunity to advance to mid-level data engineer and later to senior data engineer. Here you can expect salaries of up to 120,000 €. But of course, this requires several years of experience and comprehensive knowledge of a wide range of technologies and infrastructures. You should also be an expert in at least one programming language.



Do you have further questions about Data Engineering, or are you thinking about a job change within the IT industry? Then get in touch with us. We will be happy to advise you and connect you with the companies that really suit you!