Data Engineer

Forward Role; Cheltenham; On-site; GBP 55,000 - 85,000; Full time

Job summary

Forward Role is recruiting a Data Engineer for mid/senior level work across London and other UK locations. The role focuses on owning architecture and building cloud-native data platforms for defence, government and national security clients, with datasets that are large, multi-source, and complex.

You will collaborate with data scientists, software engineers and architects, and you will engage directly with clients to ensure the platform meets real-world needs.

Qualifications

• Must have commercial data engineering experience.
• Strong Python and SQL; hands-on Spark.
• Cloud-native platform experience on AWS or Azure.
• Comfortable with Docker, Kubernetes and CI/CD.

Responsibilities

• Design and own cloud-native data platforms and pipelines.
• Work with data scientists, software engineers and clients to define architecture.
• Ingest data from diverse sources and enable analytics, AI and ML workloads.

Skills

Python

SQL

Spark

Cloud-native

CI/CD

Docker

Kubernetes

Tools

Docker

Kubernetes

CI/CD tools

Job description

Title: Data Engineer

Location: London, Cheltenham, Manchester or Surrey
Salary: £55,000 - £85,000 plus DV bonus
Clearance: Active eDV required
Level: Mid/Senior

Tired of keeping pipelines alive? Move into a role where you own the architecture.

A defence and national security consultancy is looking for a senior engineer to own the design of its cloud-native data platforms. 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

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.