Secure Data Engineer

Developing modular, maintainable software components that process, transform and expose data for analytical, operational or AI-driven use cases, with testing and observability built in from the start.

Streaming and real-time architecture Designing and implementing data ingestion and event-driven patterns that support real-time or near-real-time flows, keeping them reliable under demanding operational conditions.

Workflow orchestration Defining data workflows programmatically and managing complex dependencies, scheduling and error-recovery behaviour within secure, assured environments.

Operationalising Data flows for AI in secure environments Packaging, deploying and monitoring ML and, increasingly, GenAI and agentic data workloads inside assured environments. This can include self-hosted or open-weight models, retrieval (RAG) and vector search over governed data, and the guardrails, evaluation and human oversight that non-deterministic systems demand. Running these patterns where internet-connected AI services are not available is a core part of the role’s future.

Deployment and ownership Containerising your own services and deploying them into secure Kubernetes or cloud environments, using CI/CD principles adapted for Defence delivery. You’ll own your applications in production and contribute to secure deployment patterns.

Resilience, observability and compliance Implementing health monitoring, structured logging, metrics and lineage to meet Defence requirements for auditability, security and operational assurance, including observability for non-deterministic and agentic behaviour. You’ll design systems that can self-heal or fail gracefully when needed.

Infrastructure as Code Provisioning the resources your services depend on using Infrastructure as Code, working closely with platform teams to stay aligned with accredited Defence architectures

What You Will Bring

You’ll be a strong, code-first data engineer who takes ownership of what you build and is energised by hard constraints rather than put off by them. You don’t need to have shipped GenAI or agentic systems in secure environments already, but you should be genuinely interested in the problem and have the engineering foundations to take it on.

Essential:

• Core engineering. Expert-level Python and strong software engineering foundations: object-oriented design, automated testing and version control.
• Data engineering stack. Building pipelines with streaming frameworks, distributed processing engines and relational or analytical storage (for example Kafka, Spark, PostgreSQL).
• Orchestration. Defining and running data workflows with modern orchestration frameworks (for example Airflow, Dagster or Prefect).
• Data quality and lineage. Tools and techniques for data testing, documentation and lineage (for example Great Expectations or dbt).
• AI, MLOps and emerging LLMOps. Operationalising ML in production (model packaging, monitoring, controlled deployment), and an understanding of how serving GenAI and agentic systems differs, including evaluation, guardrails and observability for non-deterministic outputs.
• Containerisation and Kubernetes. Confidence deploying applications in containerised environments, including defining services, pods and deployment configurations (for example Docker and Kubernetes).
• DevOps mindset. Hands-on CI/CD experience and a belief in owning the services you build (for example GitLab CI, GitHub Actions or Argo).Hands-on experience delivering data pipelines and data platforms in production environments.

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., * Infrastructure as Code or configuration management (for example Terraform or Ansible).

• Experience in secure, restricted or air-gapped environments, including Defence networks or MODCloud-aligned platforms.
• Familiarity with Google Distributed Cloud (GDC) or other edge and on-premises platforms used in constrained or disconnected settings.
• Exposure to GenAI and agentic building blocks: RAG, vector search, LLM orchestration, or LLM evaluation and observability.
• Experience using AI coding assistants within strict data-handling and security boundaries, recognising that connected AI tooling is not always available on secure networks

As a Data Engineer at Capgemini, you’ll design, build and operate reliable, scalable data pipelines and data products: the trusted foundations that power analytics, AI, GenAI and increasingly agentic systems. You’ll work hands-on with modern data engineering tooling and cloud services to ingest, transform and serve data, and you’ll prepare and govern the datasets that AI models and AI agents depend on to behave safely and correctly. You’ll also use AI-assisted engineering to accelerate your own delivery, while keeping clear human ownership of every outcome.

You’ll be part of the Data Platforms team within the Insights and Data Global Practice, which has seen strong, sustained growth across a wide range of sectors. Data Platforms is home to Data Engineers, Platform Engineers, Solution Architects and Business Analysts driving our customers’ digital and data transformation on modern cloud platforms. We specialise in the latest frameworks, reference architectures and technologies across AWS, Azure and GCP, and data platforms such as Databricks and Snowflake., Specialist tracks (we’d love depth in one of these; you don’t need all of them):

• Azure / Databricks Azure Data Lake Storage, Databricks, Apache Spark, Delta Lake, Azure Data Factory, MLflow; and, for the AI edge, Databricks Mosaic AI, Vector Search and Unity Catalog, or Azure OpenAI, Azure AI Foundry and Azure AI Search.
• AWS: Glue, Lambda, Step Functions, Kinesis, EMR, Athena, Redshift, S3 data lakes; and, for the AI edge, Amazon Bedrock, Knowledge Bases for Bedrock

About Capgemini

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.