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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Associate Data Fabric Engineer - **Company:** QINETIQ LIMITED - **Location:** Farnborough, UK - **Contract:** Permanent contract - **Skills:** Microsoft Access, Artificial Intelligence, Data Integration, Software Design Patterns, Distributed Systems, Metadata, Performance Tuning, Apache Spark, Microsoft Fabric, Apache Flink, Deployment Automation, Video Streaming, Stream Processing - **Published:** August 30, 2026 - **Apply:** https://www.collegerecruiter.com/job/2815363945-associate-data-fabric-engineer ## About the Role * Previous experience as a data/platform engineer working on distributed systems in production * Proven hands-on experience with open-source data integration and streaming technologies and delivering configurable integration patterns (connectors, reusable components, rules, and orchestration) * Previous experience enabling workflow automation for technical users * A strong grasp of data modelling fundamentals, data contracts and schema evolution * Experience designing for reliability, scalability, and operability: monitoring, alerting, deployment automation, incident handling, and performance tuning * A practical security mindset: least privilege, auditability and designing controls that do not restrict usability, We value difference and we don't have a fixed idea when it comes to background or education, provided you can show the required level of experience and willingness to learn then we would like to hear from you. ## Description As an Associate Data Fabric Engineer, you will further design, build and operate our secure, open, and extensible data fabric capability for Defence and National Security customers. Day-to-day, you will create a shared "paved road" that lets teams integrate, govern, and consume data products both safely and quickly across batch, streaming and analyst-driven workflows. This includes enabling workflow automation and configurable data integration with resilient operation in Denied, Degraded, Intermittent and Limited (DDIL) communications environments supporting edge deployments. Your responsibilities will include: * Designing, implementing, and evolving the event-driven data fabric architecture that enables automation and downstream AI-based systems: ingestion, integration, storage, processing, metadata, governance, and access patterns aligned to customer outcomes and security constraints * Supporting with building and operating workflow automation capabilities that enable repeatable, governed analyst workflows (reusable patterns, configuration-driven behaviours, and safe promotion across environments) * Delivering configurable data integration through curated connectors and standard patterns for transformation, routing, enrichment and correlation with clear contract and operational guardrails * Engineering for DDIL and edge deployments: Design Patterns for constrained connectivity, supporting appropriate local processing at edge and implementing secure configuration distribution * Building streaming ingestion, event pipelines, batch/stream processing workloads using both distributed event streaming technologies and Apache Flink and/or Apache Spark with clear SLA's and repeatable deployment * Implementing metadata, lineage and catalogue capabilities and implementing policy-based security and governance ## Related Videos - [Why and when should we consider Stream Processing frameworks in our solutions](https://www.wearedevelopers.com/videos/1085-why-and-when-should-we-consider-stream-processing-frameworks-in-our-solutions) - [Data Analytics with Microsoft Fabric: End-to-End Use Case with Data Agents](https://www.wearedevelopers.com/videos/1547-data-analytics-with-microsoft-fabric-end-to-end-use-case-with-data-agents) - [A Data Mesh needs Open Metadata](https://www.wearedevelopers.com/videos/505-a-data-mesh-needs-open-metadata) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Building the platform for providing ML predictions based on real-time player activity](https://www.wearedevelopers.com/videos/944-building-the-platform-for-providing-ml-predictions-based-on-real-time-player-activity) - [Data Fabric in Action - How to enhance a Stock Trading App with ML and Data Virtualization](https://www.wearedevelopers.com/videos/253-data-fabric-in-action-how-to-enhance-a-stock-trading-app-with-ml-and-data-virtualization) ## Related Articles - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)