Capgemini is characterized by a predominantly collaborative culture, placing people, trust and teamwork at the core of its practices. A close management approach fosters guidance, empowerment and skills development within a supportive environment. This culture is reinforced by a strong organizational dimension, ensuring process rigor, reliability and operational efficiency. It is complemented by a competitive component focused on performance and customer satisfaction, while a more moderate innovation dimension supports the continuous evolution of services and expertise.

Within our project teams, you will contribute to the development of innovative digital solutions for major clients in the banking, insurance, energy, and industry sectors. You will join a multidisciplinary team, organized in an Agile mode, working on high-impact business projects.

As a Databricks Data Engineer, you will be involved in various tasks such as:

• Designing and developing efficient and scalable data pipelines on Databricks.
• Leveraging advanced Apache Spark functionalities via PySpark for big data processing.
• Participating in data modeling, performance optimization, and best practice implementation.
• Collaborating with Data Science, BI, and business teams to meet business needs.
• Integrating solutions into CI/CD environments, using tools like Git, Azure DevOps, Terraform.
• Providing technical guidance to junior profiles and contributing to technological watch.

Graduate with a Master's degree (Bac+5) in computer science or equivalent.

Confirmed experience (>= 5 years) in a similar position.

Confirmed experience in data engineering with Databricks.

Proficiency in PySpark, SQL, and cloud environments (Azure, AWS, or GCP).

Knowledge of orchestration tools (Airflow, Azure Data Factory, etc.).

Analytical skills, autonomy, rigor, and ability to work in a team.

A Databricks or cloud certification is a plus.