Data & ML Platform Engineer

Cari; København; Hybrid; DKK 687,000 - 945,000; Full time

Job summary

Lundbeck is seeking a Data/ML Engineer to build the data foundation for commercial AI use cases and own MLOps infrastructure that moves models into production. You will join GCX and focus on data engineering to ingest and standardize data across sources like Veeva, Marketing Cloud, and Snowflake.

As models mature, you’ll expand into MLOps—pipelines, deployment, monitoring and reliability—while supporting cloud platforms (Azure/AWS) and IaC with Terraform.

Qualifications

• Degree in computer science, data engineering, software engineering or a related quantitative field.
• Several years of experience building data pipelines and integrating data across complex enterprise systems.
• Strong Python and SQL, with good engineering practice (Git, testing, documentation, reusable code).
• Experience with cloud platforms and infrastructure as code.
• Experience with Docker and Kubernetes.
• Understanding of data modeling, taxonomy design 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

• Data engineering: ingest and integrate data from Veeva, Marketing Cloud, Snowflake and other commercial systems.
• Design a shared taxonomy and data model across fragmented sources.
• Ensure data quality, consistency and reliability for analytics, ML and GenAI use cases.
• Partner with Data Scientists and GenAI Engineers to deliver clean data.
• Partner with BI to align reporting and pipelines on a common foundation.
• MLOps: build CI/CD pipelines for model deployment, experiment tracking and model registries (e.g. MLflow).
• Set up model monitoring for performance, drift and reliability in production.
• Support containerization and deployment via Docker and Kubernetes or cloud platforms.
• Establish best practices for moving models from prototype to production.
• Manage cloud infrastructure (Azure and/or AWS) and apply Terraform for reproducible environments.
• Implement security, access control and secure coding practices across the data/ML stack.
• Automate operational tasks and maintain Linux-based environments.

Skills

Python

SQL

Data pipelines

Cloud platforms

Docker

Kubernetes

Terraform / IaC

Data modeling

Taxonomy design

Data quality

Communication

ML / GenAI infra (concept)

MLflow

Snowflake

Veeva

Salesforce Marketing Cloud

Education

Degree in computer science, data engineering, software engineering or related quantitative field

Tools

Snowflake

Veeva

Salesforce Marketing Cloud

MLflow

Terraform

Job description

Lundbeck is seeking a Data/ML Engineer to build the data foundation for commercial AI use cases and own MLOps infrastructure that moves models into production. You will join GCX and focus on data engineering to ingest and standardize data across sources like Veeva, Marketing Cloud, and Snowflake.

As models mature, you’ll expand into MLOps—pipelines, deployment, monitoring and reliability—while supporting cloud platforms (Azure/AWS) and IaC with Terraform.