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# Data Engineer

- **Company:** Neweasy
- **Location:** London, 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, Information Engineering, Data Infrastructure, Data Integration, Extract Transform Load (ETL), Data Migration, Data Warehousing, Python (Programming Language), Meta-Data Management, Cloud Services, SQL Databases, Data Streaming, Feature Engineering, Apache Spark, Data Lakes, Kubernetes, Data Management, Machine Learning Operations, Data Pipelines, Devsecops, Docker
- **Published:** October 7, 2026
- **Apply:** https://www.reed.co.uk/jobs/data-engineer/57431494

## 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.

As an industry leading, nationwide Marketing, Digital, Analytics, IT and Design recruitment agency, we are continually receiving new assignments to work on, so keep a close eye on our website, Facebook, LinkedIn and Twitter pages for a full list of current permanent and interim opportunities as well as marketplace news and fun stuff.
Forward Role is operating as an employment agency.
Skills

 * Amazon Web Services (AWS)
 * Cloud Services & Infrastructure
 * Communication Skills (Key)
 * Data Engineering Roles
 * Data Pipeline Development
 * Data Warehousing
 * ETL (Extract Transform Load)
 * Microsoft Azure (Line)
 * Python Programming (Beginner)
 * SQL Databases

## 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