AI & data platform engineer in asset management

We are seeking a technically driven and motivated Data Platform Engineer to join our AI and Data Innovation team. This role is central to building and evolving our internal data platform, with a focus on data engineering tooling, pipeline automation, and platform enablement. You will be hands-on from day one, contributing to the infrastructure that powers both our data capabilities across the organization.

• Enabling teams by design, building and maintaining 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 shared 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 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.
• 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.
• Fluent in English. (Other European language is considered an asset)
• Software Engineering or Data Engineering skills.
• Platform Engineering or Cloud Engineering skills.
• Good understanding of the interplay between ways of working, architecture, and organizational outcomes.
• Relevant track record of technology-fueled business outcomes.
• Будет плюсом: Have had some exposure to workflow orchestration concepts — familiarity with Airflow or similar tools is a plus, not a requirement; Other European language is considered an asset; Experience working with Python in a data engineering capacity; Experience with SQL; Building and deploying containerized Docker applications on Kubernetes; Familiarity with Snowflake; Familiarity with DBT and DLT; Building DAGs with Apache Airflow; AWS or other public cloud experience; Infrastructure-as-code tooling, such as Terraform; Experience working in end-to-end delivery teams; Experience with a Data Mesh strategy; Prior experience with data pipelines or platform engineering projects — academic or professional — will be valued

• It’s expected you to work 100% on-site in the first 6 months (with flexibility) and after on an Hybrid regime (3 days in the Office + 2 days from home).