Databricks Data Platform Engineer

About the role

As part of the scale‑up of a modern data platform for a fast‑growing industrial company, we are looking for a Databricks Data Platform Engineer. You will support the industrialisation, scalability and reliability of a Databricks‑based environment.

Key responsibilities

• Contribute to the industrialisation of the Databricks platform.
• Design, develop and optimise scalable data pipelines.
• Implement robust data‑engineering patterns using Spark, PySpark and SQL.
• Support the structuring of a lakehouse architecture based on Databricks.
• Improve performance, reliability, monitoring and maintainability of data workflows.
• Contribute to data quality, access management and platform governance.
• Collaborate with integration teams on application connectivity and data ingestion.
• Support deployment practices, documentation and platform standards.
• Translate business and technical requirements into scalable data solutions.

Required profile

• Technical and pragmatic, able to develop, structure and scale a Databricks environment in a complex enterprise.
• Comfortable working on both implementation and platform‑improvement topics.
• Understanding of building reliable, maintainable pipelines for long‑term use.
• Experience in industrial, manufacturing, pharmaceutical or regulated environments is a plus but not mandatory.

Required skills

• Strong hands‑on experience with Databricks.
• Solid experience in data engineering and cloud‑based data platforms.
• Good knowledge of Spark, PySpark and SQL.
• Experience with Delta Lake, lakehouse architecture or modern data platforms.
• Understanding of data‑pipeline industrialisation, scalability and performance optimisation.
• Experience with cloud environments, ideally Microsoft Azure.
• Knowledge of CI/CD, Git, DevOps practices or Infrastructure as Code (asset).
• Good understanding of data governance, data quality and security principles.
• Fluent English.