We’re building the future of medical dermatology by focusing on unmet patient needs and giving people the space to think independently, take ownership and make an impact that matters.
Our purpose is simple: to transform patients’ lives by addressing real needs. We work with care, act with courage, keep things simple and focus our innovation where it makes a difference.
Recognised as a Top Employer in Spain since 2008 and in Germany since 2025, we continue to invest in an environment where people can grow and move forward.
If you care differently, you belong here.
Core Responsibilities
- Support the development, testing and documentation of data pipelines and curated datasets on Databricks.
- Clean, transform and prepare data from operational systems (SAP, manufacturing, serialization).
- Support data quality checks and documentation of datasets and workflows.
- Write code in repositories (git) following good coding practices.
- Support exploration of AI and Generative AI tools.
Learning Objectives
- Master Lakehouse / medallion architecture, Delta Lake and Unity Catalog governance.
- Learn production data engineering practices: testing, CI/CD and the DevOps lifecycle on Databricks.
- Understand industrial operations in pharmaceutical companies and regulatory constraints.
- Develop communication skills for business stakeholders.
Required Education and Experience
- Bachelor’s or Master’s degree (or currently studying) in Data Engineering, Computer Science, Data Science, Engineering, Mathematics or a related field.
- Programming in Python (familiarity with PySpark is a strong plus).
- Solid SQL (queries, joins, aggregations).
- Understanding of data structures and data modeling concepts.
- Good coding practices: use of code repositories (git) and code documentation.
- Analytical mindset and attention to detail.
Preferred Skills & Competencies
- Exposure to Databricks, Apache Spark and Delta Lake.
- Object-oriented Python (for framework-based pipeline development).
- Familiarity with Azure (Data Factory, Storage) and cloud data environments.
Knowledge of data governance, data quality, master data management and data integrity in regulated industries. - Familiarity with visualization / BI tools (Power BI) for data consumption.
- Interest in AI, Generative AI and digital transformation in manufacturing.
- Curiosity, problem-solving mindset, collaborative approach and good teamwork skills.
The Operations Data Engineering Trainee will give support to developing and maintaining data pipelines and curated datasets that improve decision-making and operational efficiency across Industrial Operations. The role will contribute to improving operational efficiency and decision-making while building foundational capabilities in data engineering and analytics.
