{"id":1799657,"url":"https://alion.io/job/capgemini-secure-data-engineer","title":"Secure Data Engineer","company":{"id":145,"name":"Capgemini","domain":"capgemini.com","url":"https://alion.io/company/capgemini","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"B","score":80,"open_postings":1815,"ghost_share":0.002,"stale_share":0.807,"repost_share":0.013,"time_to_fill_p50_days":29,"computed_at":"2026-10-31T05:58:55Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Birmingham, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":54000,"max_usd":123000,"period":"year","method":"role_country_seniority_unknown","sample_n":202},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"GitLab CI","optional":false},{"name":"Great Expectations","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"Airflow","optional":true},{"name":"Amazon Kinesis","optional":true},{"name":"Amazon Redshift","optional":true},{"name":"Amazon S3","optional":true},{"name":"Ansible","optional":true},{"name":"Apache Kafka","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"AWS Lambda","optional":true},{"name":"AWS Step Functions","optional":true},{"name":"Azure Data Factory","optional":true},{"name":"Configuration Management","optional":true},{"name":"Dagster","optional":true},{"name":"Delta Lake","optional":true},{"name":"LLM","optional":true},{"name":"MLFlow","optional":true},{"name":"OpenAI","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Prefect","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-02-24T19:00:14Z","employer_posted_date":"2026-10-18","last_verified_at":"2026-11-01T02:47:51Z","board_verified":true,"closed_at":null,"days_open":249,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":248},"description":"Who You Will Be Working With...\nAs 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.\nYou'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.\nPLEASE NOTE:\nSecurity Clearance: To be successfully appointed to this role, must be eligible to obtain Security Check (SC)clearance or DV (Developed Vetting clearance).\nTo 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.\nThroughout the recruitment process, you will be asked questions about your security clearance eligibility such as, but not limited to, country of residence and nationality.\nSome posts are restricted to sole UK Nationals for security reasons; therefore, you may be asked about your citizenship in the application process.\nThe Focus Of Your Role\nWe're looking for a code-first Data Engineer to design and build scalable, resilient data applications for Defence customers. This role sits at the intersection of software engineering and data engineering, working on mission-critical systems where reliability, security and auditability are essential.\nYou'll build data applications that process high-volume, high-velocity data, orchestrate complex workflows, and deploy your own solutions into secure containerised environments. You'll work across the full lifecycle, from development through to production, shaping architectures that support operational users, analytics and AI-enabled capabilities. Increasingly that includes GenAI and agentic systems that must run inside secure, and sometimes disconnected, environments, which is a genuinely hard problem and a distinctive part of what makes this role interesting.\nThis role is hands-on and engineering-led. You'll write production-grade code, contribute to secure platform patterns, and make sure your data services run predictably in tightly governed Defence environments..\nWhat You Will Be Doing\nBuilding data applications\nDeveloping 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.\nStreaming and real-time architecture\nDesigning and implementing data ingestion and event-driven patterns that support real-time or near-real-time flows, keeping them reliable under demanding operational conditions.\nWorkflow orchestration\nDefining data workflows programmatically and managing complex dependencies, scheduling and error-recovery behaviour within secure, assured environments.\nOperationalising Data flows for AI in secure environments\nPackaging, 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.\nDeployment and ownership\nContainerising 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.\nResilience, observability and compliance\nImplementing 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.\nInfrastructure as Code\nProvisioning the resources your services depend on using Infrastructure as Code, working closely with platform teams to stay aligned with accredited Defence architectures\nWhat You Will Bring\nYou'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.\nEssential:\nCore engineering. Expert-level Python and strong software engineering foundations: object-oriented design, automated testing and version control.\nData engineering stack. Building pipelines with streaming frameworks, distributed processing engines and relational or analytical storage (for example Kafka, Spark, PostgreSQL).\nOrchestration. Defining and running data workflows with modern orchestration frameworks (for example Airflow, Dagster or Prefect).\nData quality and lineage. Tools and techniques for data testing, documentation and lineage (for example Great Expectations or dbt).\nAI, 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.\nContainerisation and Kubernetes. Confidence deploying applications in containerised environments, including defining services, pods and deployment configurations (for example Docker and Kubernetes).\nDevOps 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.\nNice to have:\nInfrastructure as Code or configuration management (for example Terraform or Ansible).\nExperience in secure, restricted or air-gapped environments, including Defence networks or MODCloud-aligned platforms.\nFamiliarity with Google Distributed Cloud (GDC) or other edge and on-premises platforms used in constrained or disconnected settings.\nExposure to GenAI and agentic building blocks: RAG, vector search, LLM orchestration, or LLM evaluation and observability.\nExperience using AI coding assistants within strict data-handling and security boundaries, recognising that connected AI tooling is not always available on secure networks\nSpecialist tracks (we'd love depth in one of these; you don't need all of them):\nAzure / 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.\nAWS: Glue, Lambda, Step Functions, Kinesis, EMR, Athena, Redshift, S3 data lakes; and, for the AI edge, Amazon Bedrock, Knowledge Bases for Bedrock\nAbout Capgemini\nHybrid 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.\nIf 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)\nWhat we’ll offer you\nYou 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.\nYou 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.\nWhy we’re different\nAt 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.\nCapgemini 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. 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