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 Agile mode, working on projects with a strong business impact.

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

• Designing and developing efficient and scalable data pipelines on Databricks.


• Leveraging the advanced features of Apache Spark via PySpark for big data processing.


• Participating in data modeling, performance optimization, and the implementation of best practices.


• 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.
   

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


• Proven experience (≥ 5 years) in a similar role.


• Proven 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.