Data Architect - GCP
Capgemini
We are seeking a hands-on GCP Data Architect to design and build scalable, cloud-native data solutions on Google Cloud Platform. This role requires strong expertise in BigQuery, Dataflow, Pub/Sub, and Python , along with a solid understanding of distributed systems, batch and streaming data architectures, and modern data engineering practices. The ideal candidate is both an architect and a doer, capable of guiding technical direction while actively contributing to implementation and delivery.
Key Responsibilities
- Design and architect scalable, secure, and cost-effective data platforms on Google Cloud Platform.
- Lead the development of batch and real-time data ingestion and processing solutions.
- Design modern data architectures leveraging BigQuery, Dataflow, Pub/Sub, and related GCP services.
- Evaluate and implement appropriate distributed processing and data movement patterns for varying business requirements.
- Develop and optimize enterprise-scale ETL/ELT pipelines.
- Collaborate with data engineers, application developers, platform teams, and business stakeholders.
- Provide architectural guidance for data modeling, storage, governance, performance, and scalability.
- Support cloud-native application deployment patterns using Cloud Run and/or GKE.
- Contribute to infrastructure automation, DevOps, and CI/CD best practices.
- Participate in architecture reviews, technical design sessions, and code reviews.
- Troubleshoot and optimize data processing workloads and platform performance.
Required Experience
- 7+ years of experience in Data Engineering, Data Architecture, or Cloud Data Platform development.
- Strong hands-on experience with: BigQuery, Dataflow, Pub/Sub, Python
- Experience designing and implementing data solutions on Google Cloud Platform (GCP) .
- Strong understanding of:
- Distributed systems
- Data pipeline architecture
- Batch and streaming processing patterns
- Large-scale data processing and analytics
- Experience developing and supporting production-grade data pipelines.
- Ability to balance architectural strategy with hands-on implementation.
- Strong communication and stakeholder management skills.
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