Job Description

Working Student is a long-term paid internship, that allows you to kickstart your fabulous journey in the IT world. As our Trainee you can have experience working in such areas that correspond to full-time jobs. The internship usually lasts from 6 to 24 months. We are open to flexible working hours so that you can reconcile working with us with your studies. #OpenToYou.

As a Working Student in Data Engineering, you will contribute to designing, building, and maintaining data pipelines and storage systems that support AI/ML workflows. You will work on practical tasks such as data preparation, pipeline development, and performance monitoring, gaining hands-on experience with modern data engineering tools and practices. This role offers an opportunity to learn and apply software development lifecycle (SDLC) principles in real-world AI/ML projects.

Location: Poland, Wroclaw

How You Will Contribute And What You Will Learn

As part of our team, you will:

• Assist in building and maintaining data pipelines for batch, ETL, and real-time processing.
• Support optimization of data processing and storage systems
• Participate in data transformation, enrichment, and integration workflows
• Help monitor data quality, pipeline performance, and model metrics
• Contribute to technical documentation and testing activities
• Collaborate with team members to ensure data readiness for AI/ML models
• Learn and apply best practices in software engineering and DevOps
  
  

Key Skills And Experience

Must-Have:

• Active student status at minimum one year
• Knowledge of Python programming
• Understanding of SQL and relational databases
• Familiarity with data analysis libraries (e.g., Pandas)
• Interest in data engineering principles and ETL processes
• Willingness to learn workflow orchestration tools (e.g., Airflow, Kubeflow).
• Ability to work independently on assigned tasks and communicate effectively
  
  

It would be nice if you also had:

• Exposure to AI/ML concepts and frameworks (e.g., scikit-learn, TensorFlow)
• Experience with version control (Git) and basic software engineering practices
• Familiarity with cloud platforms (AWS, Azure, GCP)
• Previous academic or project experience in data processing or analytics