23 days ago
Geneva, SwitzerlandSenior
Responsibilities
- Design, build, and optimize data pipelines and ETL/ELT processes on AWS.
- Migrate and modernize on-premises data platforms, databases, and data workloads to AWS.
- Develop scalable data models, data lakes, and analytical datasets for business and regulatory requirements.
- Ensure data quality, performance, reliability, and observability across data platforms.
- Collaborate with architects, cloud engineers, and business stakeholders on end-to-end data solutions.
- Contribute to platform industrialization through automation, testing, and deployment best practices.
Requirements
- At least 4 years of experience in data engineering, including large-scale transformation and migration projects.
- Hands-on expertise with AWS data services including S3, Glue, Redshift, Athena, EMR, Lambda, and Lake Formation.
- Advanced SQL and Python skills, with experience using Spark/PySpark for large-scale data processing.
- Experience designing and implementing modern data lake, data warehouse, and ETL/ELT architectures.
- Experience migrating data workloads from on-premises environments to AWS cloud platforms.
- Fluency in English is required; French is a strong advantage.
Benefits
- Hybrid working arrangement with flexibility and work-life balance.
- Continuous learning and personal growth opportunities.
- Five weeks of vacation.
- Employee referral bonus.
- Company shares program.
- Annual contribution to a fitness subscription.
- Collaborative, diverse, and inclusive working environment.
Tech Stack
Categories
Data Engineering
About Capgemini
Capgemini is a global IT services and consulting firm that delivers strategy, cloud, AI, software engineering, and managed services to large enterprises and public-sector clients. Founded in 1967 and headquartered in Paris, it is publicly traded on Euronext Paris and operates in 50+ countries. The group expanded its engineering capabilities by acquiring Altran in 2020, now operating as Capgemini Engineering.