Data & MLOps Engineer — Build Production Pipelines

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

Job summary

Lundbeck is building the data foundation for commercial AI use cases and will own the MLOps infrastructure to productionize models. You will join GCX's data, insights and AI team, starting with data engineering to ingest, integrate and standardize data across sources such as Veeva, Marketing Cloud and Snowflake, then expand into model deployment and MLOps.

As the team matures, you’ll implement CI/CD pipelines, model registries (MLflow), monitoring, and deployment support on Docker/Kubernetes and

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: build and maintain data pipelines ingesting and integrating data from systems like Veeva, Marketing Cloud, Snowflake and other commercial sources.
• Design a shared taxonomy and data model across fragmented sources to align the team on a common foundation.
• Ensure data quality, consistency and reliability for analytics, ML and GenAI use cases.
• Partner with Data Scientists and GenAI Engineer to unblock work with clean, well-structured data.
• Collaborate with BI to ensure reporting and predictive pipelines draw from the same reliable foundation.

Skills

Python

SQL

Git

CI/CD

Data modeling

Taxonomy design

Data quality

Cloud platforms

Terraform

Docker

Kubernetes

Containerization

Education

Degree in computer science or related quantitative field

Tools

Docker

Kubernetes

Terraform

MLflow

Snowflake

Veeva

Salesforce Marketing Cloud

Job description

Lundbeck is building the data foundation for commercial AI use cases and will own the MLOps infrastructure to productionize models. You will join GCX's data, insights and AI team, starting with data engineering to ingest, integrate and standardize data across sources such as Veeva, Marketing Cloud and Snowflake, then expand into model deployment and MLOps.

As the team matures, you’ll implement CI/CD pipelines, model registries (MLflow), monitoring, and deployment support on Docker/Kubernetes and