Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow
We are looking for a Data Engineer - Working Student to join us in Berlin/Konstanz at a well-funded, growing open-source startup to help us to continue to build exciting technology that makes our users happy.
What we offer
What we’re looking for
Your tasks:
As a part-time working student, you should be available for at least 6 months. The maximum weekly working time is 20hrs/week during the semester and 40hrs/week during semester breaks. In order to apply, please, attach a CV, a cover letter, and an enrollment university certificate, and Maria will be in touch with you as soon as possible.
Apply for this position
At KNIME, we are merging science with state-of-the-art technology. We are looking for enthusiastic people who are interested in helping to continue building great software and supporting our awesome user base. We offer competitive salaries, a chance to be part of a rapidly growing, young company, and the opportunity to participate in one of the hottest technology areas out there: making sense of data!
We are not really looking for people who bring along predefined skill sets - instead, we are searching for new members of a highly interactive team, people who enjoy being part of an evolving project and seeing their job description evolve over time while the company grows. If you don't find a job that fits your skill set below but you'd love to help us make a difference, feel free to reach out and explain how you could fit into the team.
KNIME provides software for fast and intuitive access to advanced data science. At the core is the open source KNIME Analytics Platform, a visual workbench providing a wide range of state-of-the-art analytics tools and techniques to handle any use case — from basics to highly advanced. It is complemented by the commercial KNIME Server which makes data science productive in the enterprise, while staying in the same software environment for deploym