Nordea, AI & Data Engineer Trainee

• Design, build and maintain data platform tooling and infrastructure, leveraging technologies such as Snowflake, dbt, dlt and Airflow.
• Develop and maintain CI/CD pipelines to support reliable and automated data workflows.
• Contribute to infrastructure-as-code practices using Terraform and containerisation with Docker.
• Support the adoption and enablement of the data platform across business and technical teams, producing documentation and reusable components.
• Act as a technical bridge between the data platform and the AI Lab, ensuring data infrastructure is ready to support AI and LLM-driven use cases.
• Collaborate with engineers and data practitioners to identify improvements and new capabilities within the platform.

• Are eager to learn and get things done — you enjoy building things and are not afraid to get your hands dirty.
• Have a foundational understanding of data engineering concepts and curiosity about how modern data platforms work.
• Can write Python code and are comfortable picking up new libraries and tools as needed.
• Have had some exposure to workflow orchestration concepts — familiarity with Airflow or similar tools is a plus, not a requirement.
• Have a basic awareness of CI/CD practices and what good software delivery looks like.
• Are curious about AI and LLM tooling (RAG, agentic workflows, etc.) — no hands-on experience needed, just genuine interest.

The following soft skills: 

• Curious and self-driven with strong interest in data science and AI technologies.
• Ability to work collaboratively in a cross-functional team environment.
• Efficient and delivery oriented.
• Persistent and determined.
• Innovative and like to sick new ways to solve issues.

Your experience and background:

• Fluent in English. (Other European language are considered an asset).
• Master in Software Engineering, Computer Science, Maths or other numerate subject.
• Familiarity with one or more of the following is a plus: Snowflake, dbt, dlt, Terraform, Docker, Kubernetes.
• Prior experience with data pipelines or platform engineering projects — academic or professional — will be valued.