We live in an era overflowing with data but still in need of clear, usable insights. Data engineering has emerged as a vital discipline that focuses on collecting, organizing, and preparing large-scale datasets so they can drive innovation and discovery. This field underpins the latest advances in industry and research by making data accessible, reliable, and ready for analysis.
This online Master’s programme, delivered by the School of Computer Science & Engineering, dives into both the theoretical foundations and practical technologies of modern data engineering. The curriculum combines the analytical breadth of data analytics and data science with hands-on technologies for data acquisition, curation, storage and management — including databases, data warehouses, big data pipelines, and cloud computing — so you learn to design and operate systems that turn raw data into actionable knowledge.
Designed for students who want to work at the intersection of data science and systems engineering, the programme builds competencies in building scalable data infrastructures, ensuring data quality, and enabling downstream analytics and machine learning workflows. Its online format makes it accessible to international students seeking advanced training in the technical and methodological aspects of handling large, complex data ecosystems.
Overview
This master's curriculum concentrates on the technical foundations and practical methods required to build, secure, analyze and communicate insights from large-scale data systems. It combines theory (mathematics, statistics, machine learning) with hands-on technologies for data acquisition, storage, processing and visualization. Ethical, legal and privacy considerations are integrated so graduates can design responsible, compliant data solutions.
The programme emphasizes scalable and efficient processing through courses in parallel and distributed computing and advanced databases, while also covering domain-specific techniques such as image processing and natural language processing (text analysis). Students gain experience with modern tooling (including database management in Python), data acquisition pipelines, and end-to-end analytics workflows that culminate in effective visual communication and data storytelling.
Key modules (core components and Methods area)
Learning outcomes
Below is a clear summary of the documents you will need to apply. These materials help the admissions committee assess your academic background, motivation for the program, and language readiness. Prepare each item carefully and ensure transcripts are in English or German to avoid processing delays.
A statement of purpose and a CV give insight into your goals and experience; letters of recommendation provide external evaluations of your academic or professional strengths. If you cannot supply your final Bachelor’s degree certificate at the time of application, you may submit an equivalent document later. For the English language certificate, follow the program’s guidance on accepted tests and required scores.
Required application documents:
Winter Semester (International)
15 July 2026
Winter Semester (EU/EEA)
15 July 2026
Graduates are prepared for technical roles that build and maintain data infrastructure and enable data‑driven decision making. Typical positions include data engineer, data architect, cloud data specialist, big data developer, database administrator and ML operations engineer across sectors such as technology, finance, healthcare, consulting and the public sector. The programme’s combination of systems, analytics and governance training also provides a pathway into research or PhD programmes focused on data systems and applied machine learning.
Hochschule für Technik Stuttgart - University of Applied Sciences — Stuttgart
University of Regensburg — Regensburg
University of Bonn — Bonn
Brandenburg University of Technology Cottbus-Senftenberg — Cottbus