Data Architect - GCP

Capgemini; Chicago (IL); On-site; USD 120,000 - 180,000; Full time

Job summary

Capgemini seeks a hands-on GCP Data Architect to design and build scalable, cloud-native data solutions on Google Cloud Platform. This role blends architecture with implementation, guiding technical direction while actively delivering.

The ideal candidate has 7+ years in data engineering/architecture, hands-on BigQuery, Dataflow, Pub/Sub, Python, and strong knowledge of distributed systems, batch and streaming processing, and enterprise ETL/ELT pipelines.

Qualifications

• 7+ years of experience in Data Engineering, Data Architecture, or Cloud Data Platform development.
• 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 and batch/streaming processing patterns.
• Experience developing and supporting production-grade data pipelines.
• Ability to balance architectural strategy with hands-on implementation.
• Strong communication and stakeholder management skills.

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 distributed processing and data movement patterns for varied business needs.
• 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.
• 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.

Skills

Data architecture

Cloud data engineering

Stakeholder management

Communication

Tools

BigQuery

Dataflow

Pub/Sub

Python

Google Cloud Platform (GCP)

Job description

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.
• 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
• 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.