Data Engineer

Cari; København; Híbrido; DKK 687 000 - 945 000; Tempo integral

Resumo da oferta

Lundbeck seeks a Data Engineer to build the data foundation for strategic AI use cases and own the MLOps infrastructure to productionize models. You'll join GCX's data, insights and AI team and initially focus on ingesting data from multiple commercial sources, standardizing definitions and delivering a shared data model.

As models mature, your scope expands to CI/CD for deployment, model monitoring, and scalable infrastructure using Docker, Kubernetes, and Terraform across Azure and/or AWS.

Qualificações

• Several years of experience building data pipelines and integrating data across complex enterprise systems.
• Strong Python and SQL, with solid engineering practices (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.

Responsabilidades

• Data engineering: building and maintaining data pipelines that ingest and integrate data from enterprise systems.
• Designing a shared taxonomy and data model across fragmented sources for a common foundation.
• Ensuring data quality, consistency and reliability for analytics, ML and GenAI use cases.
• Partnering with Data Scientists and GenAI Engineer to unblock work with clean, well-structured data.
• Partnering with BI to ensure reporting and predictive pipelines draw from the same reliable foundation.
• Building CI/CD pipelines for model deployment and implementing experiment tracking and model registries (e.g. MLflow).
• Setting up model monitoring for performance, drift and reliability in production.
• Supporting containerization and deployment via Docker and Kubernetes or cloud platforms.
• Establishing best practices for moving models from prototype to production across the team.
• Managing cloud infrastructure (Azure and/or AWS) supporting data and ML workloads.
• Applying infrastructure as code (Terraform) for reproducible environments.
• Implementing security, access control and secure coding practices across data and ML stack.
• Automating operational tasks and maintaining Linux-based environments.

Conhecimentos

Python

SQL

Git

Testing

Documentation

Communication

Formação académica

Degree in computer science or related field

Ferramentas

Docker

Kubernetes

Terraform

Descrição da oferta de emprego

Data Engineer

Be part of building the data foundation for the key commercial strategic AI use cases, then take ownership of the MLOps infrastructure that gets our models into production. This is a chance to set the technical standard for the data and ML infrastructure at Lundbeck.

Your new role

You’ll join our newly established data, insights and AI team in GCX. In your first phase, the priority is data engineering: building ingestion and integration across fragmented commercial data sources, standardizing definitions, and creating the shared data foundation the rest of the team builds on. As models move from prototype to production, your focus expands into MLOps: pipelines, deployment, monitoring and reliability for the models.

Your responsibilities will include

Data engineering (near term focus)

• Building and maintaining data pipelines that ingest and integrate data from systems such as Veeva, Marketing Cloud, Snowflake and other commercial systems
• Designing a shared taxonomy and data model across fragmented, non standardized sources so the team is working from a common foundation
• Ensuring data quality, consistency and reliability for analytics, ML and GenAI use cases
• Partnering with Data Scientist(s) and GenAI Engineer to unblock their work with clean, well structured, accessible data
• Partnering with BI so descriptive reporting and predictive pipelines draw from the same reliable foundation rather than duplicating effort

MLOps (growing focus as the team's models mature)

• Building CI/CD pipelines for model deployment
• Implementing experiment tracking and model registries (e.g. MLflow)
• Setting up model monitoring for performance, drift and reliability in production
• Supporting containerization and deployment via Docker and Kubernetes or cloud platforms
• Establishing best practice for how models move from prototype to reliable production across the team

Platform and infrastructure

• Managing cloud infrastructure (Azure and/or AWS) supporting the team's data and ML workloads
• Applying infrastructure as code (preferably Terraform) for reproducible, version controlled environments
• Implementing security, access control and secure coding practices across the data and ML stack
• Automating operational tasks and maintaining Linux based environments

Your future team

You’ll join GCX’s newly established data, insights and AI team, responsible for the commercial intelligence layer: building competitively differentiating predictive intelligence loops. We stay close to the business to make sure what we build actually gets used, in service of reaching millions of people living with serious chronic diseases. The team is young enough that you’ll help shape its culture and ways of working. This is a build from scratch opportunity, not a seat on an established team, and this role in particular lays the foundation everyone else builds on.

What you bring to the team

• 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

Nice to have

• Hands on MLOps experience: CI/CD for models, model registries, experiment tracking (e.g. MLflow), model monitoring
• Experience with Terraform or similar infrastructure as code tooling
• Experience with Veeva, Salesforce Marketing Cloud or Snowflake specifically
• Experience in pharma, healthcare, life sciences or commercial analytics
• Familiarity with GenAI/LLM infrastructure needs (vector databases, embeddings pipelines) even without building GenAI applications directly

Our promise to you

Lundbeck offers an inspiring workplace and innovative culture, where our curiosity, accountability and adaptability enable us to transform lives. We want to go faster and further on addressing the big unmet needs of people living with brain disorders. We offer rewarding careers with a mix of exciting tasks and development opportunities that are balanced with initiatives focused on your well-being.

We need every brain in the game, and at Lundbeck, we are committed to building a workforce that is as diverse as the people we serve. Read more about our commitment at lundbeck/global/about-us/our-commitment/diversity-and-inclusion.

Annual base salary range: 687 000 - 944 900 DKK gross

At Lundbeck, we are committed to fair and transparent pay. Please note that the final base salary within the stated range will be based on relevant qualifications, skills, competencies, and level of proficiency as well as internal pay equity.

Applications must be received by November 30th, 2026

Learn more about us at lundbeck, LinkedIn or Instagram (h_lundbeck).

#EveryBrainInTheGame