Master in Data Engineering

Data is without question a crucial raw material of a modern, knowledge-based economy. Not only does it provide the basis for good management decisions or new business models, it also enables the use of AI techniques to vastly increase business efficiency.

Due to the increasing speed, variability and volume of data, the complexity of data-driven solutions is growing enormously. This degree program is designed to enable graduates to methodically design, develop, test, operate and further develop even complex and demanding data pipelines.

The program is designed to be product- and project-oriented and places particular emphasis on the topicality of the technical content, which is why it is regularly updated.

Flexible study model in trimesters that also allows for part-time employment

Teaching modern content and skills on Big Data solutions that are in demand, e.g. Apache Kafka, Spark, Flink, Airflow or Superset

Hands-on work in a modern, cloud-based programming environ-ment with 24/7 availability

International network of universities and companies

Access to innovation, sustainability and international start-ups

Specialization opportunities in the fields of AI and Data Science

  • Hands-on expertise and understanding of methodical planning and implementation of modern data-driven solutions.
  • Graduates will be able to analyze, structure, and evaluate the complex challenges of various typical Big Data scenarios, e.g., IoT, Clickstream Analysis, and Recommendation Engines.
  • Teaching of relevant skills in modern tools for storing, analyzing, processing and visualizing big data, which are in high demand on the labor market.
  • Graduates will be able to analyze and evaluate typical problems and challenges arising from the operation and further development of high-performance and complex data pipelines and develop appropriate solutions.
  • Methodical use of data in the context of modern AI-based solutions or data science analyses.
  • Analyze changes and trends in data engineering and assess their relevance to practice.
  • First university degree (bachelor or diploma) of at least 180 ECTS credits in computer science or a related field of study.
  • Very good written and spoken English.
  • Successful assessment center/admission interview (see separate information).
  • 120 ECTS (CP) are provided for the master's degree.
  • Basic modules (key competencies) = 75 CP.
  • Specialization modules = 25 CP.
  • Master's thesis = 20 CP.
  • The master's degree is taught in English.
  • The master's degree is available for full-time and part-time study.
  • The academic year is divided into trimesters

Career prospects

With the acquired data engineering competencies, graduates possess an extremely contemporary skillset that is in high demand internationally, enabling them to create and operate complex datadriven applications, products and business models in companies.

Job opportunities include primarily Chief Data Officer (CDO) or (Chief/Senior) Data Engineer positions, but also Data Architect, Big Data Engineer, Enterprise Architect, or Data Scientist.

A role as Chief Technology Officer (CTO) in data-driven startups is also possible.

In addition, there is another interesting perspective for a job as a business or technology consultant. Due to the international orientation, a worldwide activity, e.g. in Silicon Valley, is possible afterwards.
Logo of the Digital Science Foundation
Become a