GCP Python Data Engineer

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.

We are seeking a highly skilled GCP Python Data Engineer to design, build, and optimize scalable cloud-based data solutions on Google Cloud Platform (GCP). The ideal candidate will possess strong Python development skills, hands-on experience with modern data engineering technologies, and expertise in building both batch and real-time data pipelines supporting analytics, AI/ML, and enterprise reporting initiatives. Work Authorization: Candidates must be authorized to work in the United States without current or future sponsorship. No visa sponsorship, transfers, or C2C arrangements are available.

Data Engineering & Pipeline Development - Design, develop, and optimize ETL/ELT pipelines for structured and unstructured data. - Build scalable batch and streaming data processing solutions using GCP technologies. - Develop event-driven data processing solutions leveraging Pub/Sub and Cloud Functions. - Create and maintain data ingestion frameworks for enterprise data platforms. Data Storage & Analytics - Design and optimize data lake, lakehouse, and data warehouse solutions. - Build efficient data models supporting analytics, reporting, and AI/ML workloads. - Optimize performance, scalability, and cost efficiency of data pipelines and queries. Development & Automation - Develop robust Python-based solutions and frameworks. - Automate orchestration and workflow management using Cloud Composer (Airflow). - Implement CI/CD pipelines and deployment automation. - Apply software engineering best practices for testing, monitoring, and observability. Collaboration & Support - Partner with business stakeholders, analytics teams, data scientists, and engineers to deliver data solutions. - Troubleshoot production issues and perform root cause analysis. - Continuously improve reliability, scalability, security, and operational excellence.