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

Apply Now

Data Engineer

ANSON MCCADE
Manchester
1 year ago
Applications closed

Related Jobs

View all jobs

Data Engineer Data Science/Java/Python/Unix

Data Scientist / Quant Engineer

Senior Data Engineer (Data Science Team)

Senior Data Engineer - Machine Learning | Fraud & Abuse

Senior Data Engineer (Data Science Team)

Data Science and Engineering Specialist

Data Engineering Active DV Clearance Required Anson McCade is delighted to be partnering with a world-renowned consultancy as they seek to appoint Data Engineer to their talented organisation. This opportunity provides experienced individuals who are driven by curiosity and a passion for innovation, committed to building the world's leading AI-powered, cloud-native software solutions for our clients customers. With a legacy of success, our client offer global opportunities, providing a welcoming environment for those looking to advance their careers. The Data Engineer will work across product and technology ecosystem spans Research, Software, and Infrastructure, positioning you at the forefront of growth and innovation. The Data Engineer role calls for a highly analytical professional skilled in Python programming, database management, and data methodologies. Your focus will be on extracting insights from data, developing and deploying machine learning models, managing large-scale data infrastructure, and supporting the development of AI-driven products. Key Responsibilities: Data Collection and Preparation: Gather and clean data from various sources to ensure high-quality datasets that support informed decision-making. Data Analysis and Visualization: Analyze and visualize data using advanced methods to uncover patterns, insights, and trends. Statistical Analysis: Use statistical and mathematical techniques to build a solid foundation for predictive modeling. Machine Learning and AI: Design and implement machine learning and deep learning models to solve key business challenges. ML-Ops / AI-Ops: Apply ML-Ops/AI-Ops best practices to streamline model deployment and management. Big Data Management: Oversee big data infrastructure and perform data engineering tasks to ensure efficient data handling and processing. Version Control and Collaboration: Use version control tools like Git to maintain code integrity and promote team collaboration. AI-Powered Product Development: Develop, design, and support AI-based products that provide meaningful solutions aligned with business goals and user needs. Technical Skillset: Develops applications leveraging Big Data technologies, including API development. Should possess a background in traditional Application Development, along with familiarity with analytics libraries, open-source Natural Language Processing (NLP), and statistical and big data computing libraries. Exhibits strong technical skills in understanding, designing, writing, and debugging complex code. AWS (Lambda S3 DynamoDB etc) Cloudformation JavaScript Cypress testing Openshift containers

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.

AI Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we head into 2026, the AI hiring market in the UK is going through one of its biggest shake-ups yet. Economic conditions are still tight, some employers are cutting headcount, & AI itself is automating whole chunks of work. At the same time, demand for strong AI talent is still rising, salaries for in-demand skills remain high, & new roles are emerging around AI safety, governance & automation. Whether you are an AI job seeker planning your next move or a recruiter trying to build teams in a volatile market, understanding the key AI hiring trends for 2026 will help you stay ahead. This guide breaks down the most important trends to watch, what they mean in practice, & how to adapt – with practical actions for both candidates & hiring teams.

How to Write an AI CV that Beats ATS (UK examples)

Writing an AI CV for the UK market is about clarity, credibility, and alignment. Recruiters spend seconds scanning the top third of your CV, while Applicant Tracking Systems (ATS) check for relevant skills & recent impact. Your goal is to make both happy without gimmicks: plain structure, sharp evidence, and links that prove you can ship to production. This guide shows you exactly how to do that. You’ll get a clean CV anatomy, a phrase bank for measurable bullets, GitHub & portfolio tips, and three copy-ready UK examples (junior, mid, research). Paste the structure, replace the details, and tailor to each job ad.

AI Recruitment Trends 2025 (UK): What Job Seekers Must Know About Today’s Hiring Process

Summary: UK AI hiring has shifted from titles & puzzle rounds to skills, portfolios, evals, safety, governance & measurable business impact. This guide explains what’s changed, what to expect in interviews, and how to prepare—especially for LLM application, MLOps/platform, data science, AI product & safety roles. Who this is for: AI/ML engineers, LLM engineers, data scientists, MLOps/platform engineers, AI product managers, applied researchers & safety/governance specialists targeting roles in the UK.