Data Platform Engineer (DevOps)

Capgemini is an AI-powered global business and technology transformation partner with nearly 60 years of heritage, over 420,000 team members in more than 50 countries, delivering end-to-end services and solutions across strategy, technology, design, engineering, and business operations. The Insights & Data team specializes in Cloud & Big Data engineering, developing systems for processing complex datasets using cloud platforms like AWS, Azure, and GCP, and also undertakes Data Science and Machine Learning projects focused on Generative AI, NLP, Anomaly Detection, and Computer Vision.

• At least 3 years of experience in a similar role, including 1+ year working with cloud platforms.
• Strong experience in Python or another programming language.
• Hands-on experience with DevOps methodologies, especially Infrastructure as Code (Terraform, Bicep).
• Very good communication skills and strong English language skills.

Nice to have:

• Experience with CI/CD pipelines and automation tools.
• Familiarity with cloud data services such as Data Lake, Snowflake, or Synapse.
• Knowledge of containerization (Docker, Kubernetes) and cloud security best practices.
• Certifications such as AWS Certified Data Analytics, Azure Data Engineer Associate, or GCP Professional Data Engineer.

• Automate the deployment and management of cloud-based data platforms using Infrastructure as Code (IaC).
• Develop and maintain CI/CD pipelines to streamline data engineering workflows and deployments.
• Optimize system reliability, scalability, and security by implementing DevOps best practices.
• Monitor, troubleshoot, and enhance cloud-based data infrastructure to ensure high availability and performance.

Capgemini is committed to diversity and inclusion, ensuring fairness in all employment practices and evaluating individuals based on qualifications and performance, not personal characteristics.