Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Engineer (National Security Projects)

Forward Role Secure
Hampshire
1 day ago
Create job alert

Data Analytics Engineer (SC Cleared)

Location: Hampshire (hybrid working: 3 days on-site)

Level: SFIA 3 to 4 - £45,000 to £65,000 starting base + package



Some companies say they work on “important problems.”

This one actually does.


You’ll be joining an engineering team building real streaming and analytics capability used in live mission environments. This is hands-on work that directly supports people who rely on the technology, not hypothetical projects that never leave the slide deck.


If you’re an early-career or mid-level Data Engineer who wants to learn fast, get exposure to real-world scale, and progress into higher National Security work in the future, this could be the move that accelerates everything.


Clearance requirements - You must already hold SC clearance for this role.


This role also requires long-term eligibility for a higher level of National Security clearance.


For fairness and clarity, here are the baseline requirements:


• You must be a British citizen

• You must have lived in the UK for the last ten years

• You must be comfortable undergoing detailed background checks covering personal history, travel, finances and relationships


If you meet these points and value work that directly contributes to national security outcomes, you’ll be in scope.


What you’ll be doing

You’ll work in a supportive, mixed-clearance engineering team building and maintaining a high speed streaming and analytics platform. The product has been running for several years and continues to evolve, so the work is real, varied and meaningful.


You’ll be:

• Writing analytic code to filter, transform and route streaming data

• Deploying containerised components to Kubernetes

• Improving existing Go and Python analytics

• Shaping user stories and technical work with the wider team

• Monitoring and maintaining deployed systems in production environments


This isn’t a role where you just tend to dashboards. You’ll be solving problems, shipping code and learning from experienced engineers who want you to develop.


The tech you’ll work with:


• Go or Python (and support to learn Go if you come from Python or Java)

• Kafka, NATS, qpid and other message brokers

• Kubernetes, Helm, containers, CI/CD

• AWS (EKS, EC2, S3)

• Linux, Redis, Docker stacks

• AI and ML exposure is helpful but not required


You don’t need to know everything on day one. Growth is part of the package.


What they’re looking for


• Valid Clearance and Eligibility

• Background in Python or Java

• Some familiarity with Kubernetes

• Curiosity about distributed systems and data pipelines

• Ability to work with the team on site three days a week

• Graduates with strong projects or placements are encouraged to apply


This team values different backgrounds, learning styles and perspectives. If you’re someone who enjoys solving complex problems with others and wants to grow, you’ll fit.


Why this is worth your time

This is one of the few engineering environments in the NS space where junior and mid-level engineers genuinely grow quickly. People progress into Tech Lead, Cloud SME, Scrum Master and deeper technical roles because the environment is built around learning, mentorship and real responsibility.


You’ll work on technology that matters, alongside skilled engineers who take pride in supporting and developing new talent.


If you’d like to understand the culture, the team dynamics or what the long-term path looks like, please apply and/or drop me a message - I'll be happy to talk you through the opportunity further.

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.