Data Engineering Intern (All genders)

About the role

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.
The Flight Science team is responsible for analysing, processing and interpreting data generated throughout the development and testing of our autonomous systems. By building reliable data infrastructure and scalable analytics workflows, the team enables engineers to make informed decisions that improve system performance, reliability and operational capability. Working closely with software, robotics and flight test engineers, the team transforms complex engineering data into actionable insights that drive product development 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.