Data Engineering Intern (All genders)

STARK is a new kind of defence technology company revolutionising the way autonomous systems are deployed across multiple domains. We design, develop and manufacture high-performance unmanned systems that are software-defined, mass-scalable and cost-effective. This provides our operators with a decisive edge in highly contested environments.

We’re focused on delivering deployable, high-performance systems—not future promises. In a time of rising threats, STARK is bolstering the technological edge of NATO Allies and their Partners to deter aggression and defend Europe—today.

As a Data Engineering Intern / Working Student, you will help build and maintain the data infrastructure that powers the Flight Science team's analytics and decision-making. You will develop reliable data pipelines, improve data quality and collaborate with engineers to ensure flight and operational data is accessible, accurate and scalable.

• Develop and maintain automated ETL/ELT pipelines to ingest, clean and structure raw flight test and simulation data.
• Optimise data workflows to reduce processing latency and improve access to engineering analytics.
• Implement data validation and monitoring processes to identify pipeline failures, anomalies and missing data.
• Collaborate with Flight Science engineers to design scalable data models and database schemas.
• Integrate new internal and external data sources, including weather and telemetry datasets.
• Document pipeline architecture, data models and system dependencies to support maintainability and knowledge sharing.
• Support the scaling and optimisation of data storage solutions as flight data volumes increase.

• Currently pursuing a degree in Computer Science, Information Systems, Data Science or a related technical field, with at least three completed semesters.
• Strong Python programming skills for data processing and scripting.
• Solid understanding of SQL and relational databases.
• Familiarity with ETL/ELT concepts and data pipeline development through university projects, internships or personal projects.
• Experience using Git or other version control systems.
• Strong English communication skills, both written and spoken.
• Availability to work full-time for a six-month internship or part-time (15–20 hours per week) as a working student.

• Experience working with time-series or sensor data.
• Familiarity with workflow orchestration tools such as Apache Airflow or Prefect.
• Basic knowledge of cloud platforms (AWS, Azure or GCP) or Docker.
• Experience with data visualisation tools such as Grafana, Tableau or Streamlit.