Data & ML Platform Engineer: Build Production-Grade Pipelines

Lundbeck; København; On-site; DKK 687,000 - 945,000; Full time

Job summary

Lundbeck is seeking a Data/ML Engineer to build the data foundation for key commercial AI use cases and to own the MLOps infrastructure, setting technical standards across Lundbeck’s data and ML stack.

You will join the GCX data, insights and AI team, starting with data engineering to ingest and standardize data from Veeva, Marketing Cloud, Snowflake and other sources, then expanding into production-ready ML pipelines and model monitoring with Docker/Kubernetes on cloud platforms.

Qualifications

• Degree in computer science, data engineering, software engineering or related quantitative field.
• Several years of experience building data pipelines across complex enterprise systems.
• Strong Python and SQL, with Git, testing, and good documentation.
• Experience with cloud platforms and infrastructure as code.
• Experience with Docker and Kubernetes.
• Understanding of data modeling and data quality practices for messy, fragmented or newly created data sources.
• Comfort working in ambiguity, building foundational infrastructure before the full picture of downstream use cases is settled.
• Strong communication skills and the ability to partner closely with data scientists, engineers and BI colleagues.

Responsibilities

• Build and maintain data pipelines ingesting data from enterprise systems.
• Design a shared taxonomy and data model across fragmented sources.
• Ensure data quality for analytics, ML and GenAI use cases.
• Partner with data scientists and GenAI engineers to unblock their work with clean, well structured data.
• Collaborate with BI to align descriptive reporting and predictive pipelines.
• Build CI/CD pipelines for model deployment.
• Implement experiment tracking and model registries (e.g. MLflow).
• Set up model monitoring for production performance.
• Support containerization and deployment via Docker and Kubernetes or cloud platforms.
• Establish infrastructure as code for reproducible environments.
• Apply security and access controls across data and ML stack.
• Automate operational tasks and maintain Linux environments.

Skills

Python

SQL

Git

Testing

Documentation

Data pipelines

Cloud platforms

Infrastructure as code

Docker

Kubernetes

Data modeling

GenAI

Education

Degree in computer science or related field

Tools

Terraform

Docker

Kubernetes

MLflow

Snowflake

Veeva

Salesforce Marketing Cloud

Job description

Lundbeck is seeking a Data/ML Engineer to build the data foundation for key commercial AI use cases and to own the MLOps infrastructure, setting technical standards across Lundbeck’s data and ML stack.

You will join the GCX data, insights and AI team, starting with data engineering to ingest and standardize data from Veeva, Marketing Cloud, Snowflake and other sources, then expanding into production-ready ML pipelines and model monitoring with Docker/Kubernetes on cloud platforms.