> Markdown version of [/jobs/ext/3614039-data-engineer](https://www.wearedevelopers.com/jobs/ext/3614039-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** Forward Role Recruitment - **Location:** Cheltenham, UK - **Experience:** Expert - **Salary:** £50,000.0 - £85,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, Cloud Foundry, Continuous Integration, Data Infrastructure, Data Integration, Data Migration, Python (Programming Language), Meta-Data Management, SQL Databases, Feature Engineering, Apache Spark, Kubernetes, Machine Learning Operations, Devsecops, Docker - **Published:** October 8, 2026 - **Apply:** https://www.forwardrole.com/jobs/182130dataengineer/#apply-now ## About the Role Must have: commercial data engineering experience, strong Python and SQL, hands-on Spark, and cloud-native platform experience on AWS or Azure. Comfortable with Docker, Kubernetes and CI/CD. Nice to have: data modelling and metadata management, AI/ML pipelines and feature engineering. ## Description Tired of keeping pipelines alive? Move into a role where you own the architecture. The customers are in Defence, Government and National Security, and the datasets are big, messy and multi-source. You'll work closely with data scientists, software engineers and architects, and directly with the client, so you always know what the platform is for. What you'll be working on * Data platforms and pipelines, batch and streaming, built for scale * Data lakes and cloud-native architecture on AWS or Azure * Ingesting and integrating data from lots of different sources * Data foundations for analytics, AI and ML workloads * DevSecOps and Infrastructure as Code * Python, SQL, Spark, Docker, Kubernetes and CI/CD Why it's worth a look * Real influence over how platforms get designed and built * Close to the customer and the decision-makers * Exposure to AI/ML workloads, not just moving data from A to B