AI Data Engineer

Who You'll Be Working With As an AI Data Engineer at Capgemini, you will design, build and operate reliable, scalable data pipelines and data products that enable analytics, AI and GenAI. You will work hands-on with modern data engineering tools and cloud services to ingest, transform and serve trusted datasets, using AI-assisted engineering to accelerate delivery, improve quality and automate routine tasks—while applying sound judgement and secure engineering practices.

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 Data Engineers, Platform Engineers, Solutions Architects and Business Analysts who are focused on driving our customers’ digital and data transformation journey using modern cloud platforms.

We specialise in using the latest frameworks, reference architectures and technologies across a range of cloud and data platforms.

PLEASE NOTE:

Security Clearance:
To be successfully appointed to this role, you must be eligible to obtain UK 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 an AI Data Engineer, you will be an integral part of our team dedicated to building scalable and secure data platforms. You will use distributed data processing and cloud services to develop and optimise batch and streaming pipelines, implement modern lakehouse/warehouse patterns, and deliver well-governed datasets that power reporting, analytics and AI/ML use cases.

You will also use AI tooling (for example, coding assistants and automated analysis) to speed up development, strengthen testing and documentation, and improve operational excellence—within agreed security and compliance boundaries.

Build and maintain data pipelines and data products:
Use appropriate ETL/ELT and distributed processing approaches to ingest, transform and curate data in cloud-based storage and analytics platforms.

Implement data modelling, quality and governance:
Develop scalable data models, apply validation/quality checks, and use AI-assisted techniques to propose test cases, validation rules and anomaly explanations (with human review) to ensure reliable and auditable data products.

Enable AI/ML and analytics use cases:
Prepare curated datasets and features, collaborate with data scientists, and integrate pipelines with ML workflows where required, using AI assistance to speed up dataset understanding and documentation.

Monitor and optimise workloads:
Improve performance, reliability and cost efficiency, and implement observability (logging, alerting, SLAs) for production data pipelines—using AI-assisted troubleshooting to speed up triage while maintaining clear operational ownership.

Collaborate across teams:
Work with business analysts, platform engineers, data scientists and Dev Ops to deliver secure, well-tested data solutions in an agile environment, clearly communicating design decisions and AI-assisted outputs in an auditable way.

Apply engineering best practices:
Use version control, code review, automated testing and CI/CD; use AI tools to accelerate refactoring, test generation and documentation, following safe prompt practices and ensuring no sensitive data is  a data engineering advocate:
Share knowledge, contribute to accelerators and standards (including AI-enabled engineering patterns), and pursue relevant learning and certification pathways.

What You Will Bring and Your Experience You will bring strong, hands-on experience delivering modern data engineering solutions within complex environments, with an understanding of regulatory obligations and the need for…