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

Apply Now

Head Of Data Engineering & Infrastructure

London
9 months ago
Applications closed

Related Jobs

View all jobs

Junior Data Scientist

Staff Data Scientist

Staff Data Scientist

Executive Director / Principal Machine Learning Engineer

Principal Machine Learning Engineer

Head of Data Science

Location: London Office / Hybrid (3 days a week working from home)

Reporting to: Director - Data & Analytics

The Role : Head of Data Engineering & Infrastructure

This role offers a unique opportunity to lead and shape the data engineering strategy at an organisation that values the transformative power of data. As Head of Data Engineering and Infrastructure, you will drive the development of scalable and resilient data infrastructure, enabling the business to unlock insights and foster innovation. You will work at the intersection of advanced analytics, cloud technologies, and data science, playing a pivotal role in accelerating AI and delivering a unified data platform.

You will lead a high-performing team of data engineers, collaborating closely with cross-functional departments to drive business growth and enhance the customer and partner experience. The ideal candidate will bring a mix of technical expertise, leadership skills, and strategic thinking to champion data-driven initiatives that align with organisational goals.

Key Responsibilities:

Define and implement a data engineering strategy aligned with the organisation's objectives and technological advancements, ensuring scalability and adaptability.
Lead the design and delivery of a modern cloud data platform, optimising for scalability, reliability, and cost-efficiency.
Integrate emerging technologies, such as AI and real-time analytics, into the data infrastructure to enable advanced data processing capabilities.
Promote a data-driven culture by implementing best practices, shaping data governance frameworks, and fostering innovation in data processes.
Partner with senior leadership to ensure alignment between data engineering initiatives and business strategy, driving value through data-driven decision-making.
Optimise data workflows and algorithms to improve performance and reduce latency while maintaining resource efficiency.
Ensure the integrity and accuracy of data assets through robust quality assurance processes.
Lead and mentor a team of data engineers and database architects, encouraging continuous learning and professional development.
Foster collaboration with cross-functional teams to drive interdisciplinary innovation and problem-solving.
Represent data engineering in senior stakeholder meetings and external industry events, advocating for data as a strategic asset.
Build relationships with external partners and vendors to stay informed about the latest trends and technologies in data engineering.

What We're Looking For:

A strategic mindset with the ability to translate goals into actionable plans and deliver organisational change.
Proven leadership skills to inspire and support a high-performing data engineering team, building trust and fostering a culture of growth and belonging.
Expertise in end-to-end data science infrastructure development to enable AI and advanced analytics.
Strong analytical and business acumen to create impactful data and cloud solutions.
Exceptional communication and collaboration skills, with the ability to convey complex technical concepts to diverse audiences.
Proficiency in programming languages (e.g., Python, Scala, SQL) and cloud platforms (GCP, AWS, Azure), with a strong grasp of data processing and analytics services.
Results-oriented with a focus on delivering measurable outcomes, setting clear goals, and tracking progress through OKRs and KPIs.
Passion for innovation and a proactive approach to exploring new technologies and best practices.
Adaptable and resilient, thriving in dynamic and fast-paced environments.

Preferred Qualifications and Experience:

A Bachelor's or Master's degree in Engineering, Computer Science, Mathematics, or a related STEM field, or equivalent data engineering experience.
Deep understanding of data engineering methodologies, including data modelling, ETL/ELT processes, data mesh, and distributed computing.
Experience with big data technologies like Spark and Kafka, and database systems with a focus on performance optimisation.
A proven track record of building and leading successful data engineering teams and delivering impactful projects.
Demonstrated ability to balance technical feasibility with business impact and ROI in decision-making and project prioritisation

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.

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.

Why AI Careers in the UK Are Becoming More Multidisciplinary

Artificial intelligence is no longer a single-discipline pursuit. In the UK, employers increasingly want talent that can code and communicate, model and manage risk, experiment and empathise. That shift is reshaping job descriptions, training pathways & career progression. AI is touching regulated sectors, sensitive user journeys & public services — so the work now sits at the crossroads of computer science, law, ethics, psychology, linguistics & design. This isn’t a buzzword-driven change. It’s happening because real systems are deployed in the wild where people have rights, needs, habits & constraints. As models move from lab demos to products that diagnose, advise, detect fraud, personalise education or generate media, teams must align performance with accountability, safety & usability. The UK’s maturing AI ecosystem — from startups to FTSE 100s, consultancies, the public sector & universities — is responding by hiring multidisciplinary teams who can anticipate social impact as confidently as they ship features. Below, we unpack the forces behind this change, spotlight five disciplines now fused with AI roles, show what it means for UK job-seekers & employers, and map practical steps to future-proof your CV.