Senior Data Engineer (Hybrid)

About the role

Fresenius Kabi is looking for a Senior Data Engineer for a hybrid role in Barcelona, combining hands-on technical work with consulting. The engineer designs, develops and maintains scalable data pipelines and architectures, working closely with data owners, IT, analysts and business stakeholders so that data is available, high quality and accessible for analytics, reporting and advanced use cases.

Key responsibilities

• Design, build and optimise data pipelines for structured and unstructured data.
• Develop and maintain ETL/ELT processes integrating data from SAP, Salesforce, APIs, flat files and cloud platforms.
• Ensure data quality, consistency and governance across the data lifecycle.
• Implement and manage data models, warehouses and lakehouse solutions (e.g. Snowflake, Databricks, Redshift, BigQuery).
• Translate business requirements into technical solutions and monitor pipelines for scalability, performance and cost.
• Ensure compliance with data security, privacy and governance standards (GDPR, HIPAA, etc.).
• Automate repetitive tasks, follow DevOps/DataOps best practices and work with cloud platforms (AWS, Azure, GCP) and modern data stack tools.

Required profile

• Bachelor's or Master's degree in Computer Science, Information Technology, Data Engineering or a related field.
• Over 6 years of hands-on experience in data engineering, data integration or related fields.
• Proven experience designing data pipelines, data models and data platforms.
• Strong exposure to client-facing consulting roles with cross-functional stakeholders.
• Experience with enterprise systems (SAP, Salesforce, ERP, CRM) and complex data migration projects.
• Experience in regulated sectors such as Pharma, Healthcare, Finance or Manufacturing is an advantage.
• Strong communication and presentation skills, able to explain technical solutions to business leaders.

Required skills

• SQL, Python and/or Scala/Java.
• ETL/ELT tools such as Informatica, SAP Data Services, Talend, dbt, Azure Data Factory.
• Data warehouse and lakehouse platforms (Snowflake, Databricks, BigQuery, Redshift, Synapse).
• Big data frameworks (Apache Spark, Kafka, Hadoop).
• Data governance, lineage and cataloging tools (Collibra, Alation, Purview) and MDM/MDG.