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

Apply Now

AWS Data Engineer

DMG Events
London
8 months ago
Applications closed

Related Jobs

View all jobs

AWS Data Engineer

AWS Data Engineer - Inside IR35 (IT) / Freelance

AWS Data Engineer

AWS Data Engineer

AWS Data Engineer

AWS Data Engineer

IntroductionWe are looking for an experienced AWS Data Engineer to join our team to support the development of the Customer Data Platform (CDP) project. As a Data Engineer, you will work closely with the Lead Engineer, Business Analysts and third-party professional services to lay the groundwork to enable the business to truly become data-driven, creating the one source of truth for our customer data.Responsibilities• Designing, building and maintaining the data lake solution and associated pipelines • Develop and own the data strategy on coding best practices • Contribute to the overall architecture by identifying gaps and efficiencies in the design • Ensuring that data quality is considered at every point of the data journey and working closely with the business to ensure the correct rules and identifiers are in place • Coaching the junior members of the Data team to be more cloud engineering focussed • Maintaining, testing and implementing disaster recovery procedures.Skills/Qualifications

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.

Jobs

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

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Jobs

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Jobs

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.