AWS Data Solution Architect

Who You'll Be Working With

As an AWS Data Solution Architect at Capgemini, you will design and lead the delivery of modern data platforms that enable analytics, AI, GenAI, and data driven decision making  will translate business strategy into robust, governed, and AI ready architectures, taking end to end ownership from vision through to delivery and value realisation

You will be part of the Data Platforms team that is part of the Insights and Data Global Practice that has seen strong growth and continued success across a variety of projects and sectors. Data Platforms is the home of the Data Engineers, Platform Engineers, Solutions Architects and Business Analysts who are focused on driving our customers digital and data transformation journey using the modern cloud platforms.

We specialise on using the latest frameworks, reference architectures and technologies using AWS, Azure and GCP along with various data platforms like Databricks and Snowflake

Please Note:

Security Clearance: To be successfully appointed to this role, must be eligible to obtain Security Check (SC) clearance.
To obtain SC clearance, the successful applicant must have resided continuously within the United Kingdom for the last 5 years, along with other criteria and requirements.

Throughout the recruitment process, you will be asked questions about your security clearance eligibility such as, but not limited to, country of residence and nationality.
Some posts are restricted to sole UK Nationals for security reasons; therefore, you may be asked about your citizenship in the application process.

The Focus Of Your Role

As a Solution Architect with an AWS data platform focus, you will be an integral part of our team dedicated to building scalable, secure, and well-governed data platforms. You will leverage your expertise in AWS analytics and data integration services to design, develop, and implement modern data architectures and pipelines that fuel analytics, AI/ML, and data-driven operations.

• Design and build high-performance data integration solutions:
  Use AWS Glue, AWS Step Functions, Amazon Event Bridge, and AWS Database Migration Service (DMS) to orchestrate ingestion, transformation, and movement of data across on-premises and cloud sources into Amazon S3 and downstream analytics services.
• Design and oversee the delivery of secure lakehouse and warehouse architectures:
  Implement logical and physical data models, data quality controls, and governance practices using Amazon S3 (data lake), AWS Lake Formation, Amazon Redshift, and Amazon Athena to ensure trusted data.
• Ability to design AI/ML-enabled solutions:
  Enable feature-ready, governed datasets for data science and integrate with Amazon Sage Maker (and related AWS AI services) to operationalise models and drive business insights.
• Design, monitor and optimise data pipelines and platform performance:
  Use Amazon Cloud Watch, AWS Cloud Trail, and service-native monitoring to improve reliability, throughput, cost, and scalability across ingestion, storage, and analytics workloads.
• Collaborate with cross-functional teams:
  Work closely with business analysts, data engineers, data scientists, and platform/Dev Ops teams to implement secure CI/CD, environment management, and scalable multi-account patterns (e.g., landing zones) for governed delivery.
• Stay ahead of the curve:
  Continuously learn and adapt to the evolving AWS data and analytics landscape, including AWS Glue, Lake Formation, Redshift, Athena, EMR, and related best practices.
• Define the platform operating model:
  Establish AWS multi-account strategies, environment separation (dev/test/prod), naming standards, and guardrails that enable secure self-service while maintaining control and consistency.
• Support governance and metadata management:
  Implement data discovery, classification, lineage, and stewardship practices using AWS Glue Data Catalog, AWS Lake
• Formation, and aligned standards for trusted, auditable data products.
• Own security architecture for data platforms:
  Design identity, access, and secrets management (e.g., AWS IAM, IAM Identity Center, KMS, Secrets Manager) and apply network and monitoring controls appropriate for sensitive public sector workloads.
• Embed engineering excellence:
  Define CI/CD and release management for AWS data platforms, including Git-based workflows, automated testing, and repeatable environment provisioning using Infrastructure-as-Code (e.g., AWS CDK, Cloud Formation, Terraform, and AWS Code Pipeline/Code Build).
• Drive Fin Ops and capacity planning:
  Shape cost governance (tagging, chargeback/showback), monitor usage with AWS Cost Explorer and AWS Budgets, and optimise designs using the AWS Well-Architected Framework and service best practices.
• Lead migration and modernisation:
  Assess legacy estates and design pragmatic roadmaps to modern AWS architectures (including S3-based data lakes and Redshift-based…