AI Data Engineer

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.

Security Clearance

PLEASE NOTE: 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 DevOps 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 exposed.
• Be 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 trusted, mission critical data. You will be able to build secure, scalable and well‑governed pipelines and lakehouse data products that support analytics, AI and GenAI while meeting requirements for data protection, sovereignty, transparency and auditability.

You will also be confident using AI assistants responsibly to accelerate delivery (e.g. code scaffolding, test generation, documentation and troubleshooting), with clear human ownership of outcomes and adherence to security and compliance constraints. You will be comfortable collaborating with stakeholders to translate outcomes into robust data solutions, and you will bring strong engineering discipline, documentation and teamwork skills to help teams deliver sustainable data capabilities.

Experience

• Minimum 5+ years of experience as a Data Engineer, including hands‑on delivery of data pipelines and data platforms in production environments.
• Strong expertise in distributed data processing and modern data platform patterns (lakehouse/warehouse), with good understanding of data modelling, performance tuning and reliability.
• Experience with cloud services for data (storage, compute and orchestration) and with workflow/orchestration tooling (e.g., managed schedulers, data integration services, or similar).
• Proficiency in Python and SQL, with strong software engineering practices (Git, code review, unit testing, CI/CD) and an ability to troubleshoot production issues—using AI tooling appropriately to speed up analysis and remediation.
• Practical experience using AI assistants in an engineering workflow (e.g., generating code snippets/tests, improving documentation, log analysis), with good prompt hygiene and an understanding of data confidentiality and secure use of AI tools.
• A continuous learning mindset, including keeping up to date with modern data engineering practices and responsible AI‑assisted development.
• Relevant certifications (desirable): cloud platform and/or data engineering certifications.

Additional Information

Hybrid working: The places that you work from day to day will vary according to your role, your needs, and those of the business; it will be a blend of company offices, client sites, and your home; noting that you will be unable to work at home 100% of the time.

If you are successfully offered this position, you will go through a series of pre‑employment checks, including identity, nationality (single or dual) or immigration status, employment history going back 3 continuous years, and unspent criminal record check (known as Disclosure and Barring Service).

What We'll Offer You

You will be encouraged to have a positive work‑life balance. Our hybrid‑first way of working means we embed hybrid working in all that we do and make flexible working arrangements the day‑to‑day reality for our people. All UK employees are eligible to request flexible working arrangements.

You will be empowered to explore, innovate, and progress. You will benefit from Capgemini’s 'learning for life' mindset, meaning you will have countless training and development opportunities from thinktanks to hackathons, and access to 250,000 courses with numerous external certifications from AWS, Microsoft, Harvard Manage Mentor, Cybersecurity qualifications and much more.

Why we're different

At Capgemini, we help organisations across the world become more agile, more competitive, and more successful. Smart, tailored, often ground‑breaking technical solutions to complex problems are the norm. But so, too, is a culture that’s as collaborative as it is forward thinking. Working closely with each other, and with our clients, we get under the skin of businesses and to the heart of their goals. You will too.

Capgemini is proud to represent nearly 130 nationalities and its cultural diversity. Our holistic definition of diversity extends beyond gender, gender identity, sexual orientation, disability, ethnicity, race, age, and religion. Capgemini views diversity as everything that makes us who we are as an organization, including our social background, our experiences in life and work, our communication styles and even our personality. These dimensions contribute to the type of diversity we value the most: diversity of thought.

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