How to Get Started in Data Engineering

Terms like big data, data science, and machine learning are the buzzwords of this time. It is not for nothing that data is also referred to as the oil of the 21st century. But first, the right data of the right quality must be available so that something becomes possible here.

It must firstly be extracted to be processed further, e.g. into business analyses, statistical models, or even a new data-driven service. This is where the data engineer comes into play. In this article, you’ll find out everything about their field of work, training, and how you can enter this specific work area.

Tasks of a Data Engineer

Data engineers are responsible for building data pipelines, data warehouses and lakes, data services, data products, and the whole architecture that uses this data within a company. They are also responsible for selecting the optimal data infrastructure, and monitoring and maintaining it. Of course, this means that data engineers also need to know a lot about the systems in a company—only then can they correctly and efficiently connect ERP and CRM systems.

The data engineer must also know the data itself. Only then can correct ETL/ELT processes be implemented in data pipelines from source systems to end destinations like cloud data warehouses. In this process, the data is often transformed, e.g. summarized, cleaned, or brought into a new structure.

It is also important that they work well with related areas, because only then can good results be delivered together with data scientists, machine learning engineers, or business analysts. In this regard, one can see that data teams often share their data transformation responsibilities amongst themselves. Within this context, data engineers take up slightly different tasks than the other teams. However, one can say that this is the exact same transformation process as in the field of software development where multiple teams have their own responsibilities.

How to Become a Data Engineer

There is no specific degree program in data engineering. However, a lot of (online) courses and training programs exist for one to specialise in it. Often, data engineers have skills and knowledge from other areas like:

  • (Business) informatics
  • Computer or software engineering
  • Statistics and data science

Training with a focus on trending topics like business intelligence, databases, data processes, cloud data science, or data analytics can make it easier for one to enter the profession. Also, they can then expect a higher salary.

Environment of a Data Engineer: Source

Skills and Used Technologies

Like other professions in the field of IT and data, the data engineer requires a general as well as a deep technical understanding. It is important for data engineers to be familiar with certain technologies in the field. These include:

  • Programming languages like Python, Scala, or C#
  • Database languages like SQL
  • Data storage/processing systems
  • Machine learning tools
  • Experience in cloud technologies like Google, Amazon, or Azure
  • Data modeling and structuring methods

Examples of Tools and Languages used in Data Engineering – Source

It is important to emphasize that the trend in everything is running towards the cloud. In addition to SaaS and cloud data warehouse technologies such as Google BigQuery or Amazon Redshift, DaaS (data as a service) is also becoming increasingly popular. In this case, data integration tools with their respective data processes are all completely implemented and stored in the cloud.

Data Engineer vs. Data Scientist

The terms “data scientist” and “data engineer” are often used interchangeably. However, their roles are quite different. As already said, data engineers work closely with other data experts like data scientists and data analysts. When working with big data, each profession focuses on different phases. While both professions are related to each other and have many points of contact, overarching (drag and drop) data analysis tools ensure that data engineers can also take on data science tasks and vice versa.

The core tasks of a data engineer lie in the integration of data. They obtain data, monitor the processes for it, and prepare it for data scientists and data analysts. On the other side, the data scientist is more concerned with analyzing this data and building dashboards, statistical analyses, or machine learning models.


In conclusion, one can say that data engineers are becoming more and more important in today’s working world, since companies do have to work with vast amounts of data. There is no specific program that must be undergone prior to working as a data engineer. However, skills and knowledge from other fields such as informatics, software engineering, and machine learning are often required. In this regard, it is important to say that a data engineer should have a specific amount of knowledge in programming and database languages to do their job correctly.

Finally, one must state that data engineers are not the same as data scientists. Both professions have different tasks and work in slightly different areas within a company. While data engineers are mostly concerned with the integration of data, data scientists are focusing on analyzing the data and creating visualizations such as dashboard or machine learning models.

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