AI & data platform engineer in asset management
Summary
Builds and evolves Nordea Asset Management's internal data platform — data engineering tooling, pipeline automation, CI/CD and infrastructure-as-code — using Snowflake, DBT, DLT, Airflow, Python, Terraform and Docker, while acting as a technical bridge between the data platform and the AI Lab's LLM-driven use cases.
Описание
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.
Задачи
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.
Требования
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
Условия
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).