National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Head of Data Engineering

Mirai Talent
gb
1 year ago
Create job alert

Head of Data Engineering & Analytics

We are seeking a Head of Data Engineering & Analytics and play a pivotal role in leading cutting-edge data infrastructure initiatives. In this dynamic position, you will shape the future of digital insurance, utilising advanced technologies and adhering to industry best practices with tools such as AWS, Snowflake, dbt, and more.

What you’ll be doing:

Develop and execute a comprehensive data strategy that aligns with our business goals and enhances decision-making processes. Champion the integration of high-quality, accessible data throughout our operations to promote a culture of data-driven decision making. Recruit, mentor, and lead a top-tier data engineering and analytics team, emphasizing innovation, efficiency, and collaborative growth. Manage significant expansions in data volume and integration, enhancing our product offerings with superior data insights. Lead robust data governance practices, collaborating closely with compliance and other stakeholders to uphold data integrity and utility. Design, develop, and maintain sophisticated data models and architectures that inform business strategies and operational efficiencies. Spearhead the development and management of robust data pipelines in collaboration with technical teams and data scientists, supporting both pre-processing and post-processing activities.

What you can bring:

A minimum of 10 years’ experience in data engineering and analytics, with a proven ability to guide and grow high-performing teams. Strong background in data governance and the scaling of data operations. Demonstrated leadership in transformative data-driven projects and team development. Expertise in data modelling, database design, and data warehousing. Proficiency in SQL, Python, and other essential data manipulation technologies. Experience in deploying complex machine learning algorithms in partnership with data science teams.

What would be advantageous:

Experience in implementing observability and monitoring frameworks. Active engagement in open-source projects or tech communities. Experience in fast-paced, high-growth startups. Knowledge of the insurance industry.

What’s in it for you:

Competitive share options scheme. Generous pension plan and enhanced parental leave. 25 days of holiday plus public holidays. Financial support for professional development and continuous learning. Home office stipend and mental health support initiatives. Cycle to work scheme and dedicated time for professional growth. Company-provided MacBook.

Mirai believes in the power of diversity and the importance of an inclusive culture. We welcome applications from individuals of all backgrounds, understanding that a range of perspectives strengthens both our team and our partners’ teams. This is just one of the ways that we’re taking positive action to shaping a collaborative and diverse future in the workplace.

Related Jobs

View all jobs

Head of Data Engineering & Governance

Head of Data Engineering & Governance

Head of Data Engineering & Analytics

Head of Data Engineering & Governance

Head of Data Engineering & Governance

Head of Data Engineering & Governance

National AI Awards 2025

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.

Return-to-Work Pathways: Relaunch Your Machine Learning Career with Returnships, Flexible & Hybrid Roles

Returning to work after an extended break can feel like starting from scratch—especially in a specialist field like machine learning. Whether you paused your career for parenting, caring responsibilities or another life chapter, the UK’s machine learning sector now offers a variety of return-to-work pathways. From structured returnships to flexible and hybrid roles, these programmes recognise the transferable skills and resilience you’ve developed, pairing you with mentorship, upskilling and supportive networks to ease your transition back. In this guide, you’ll discover how to: Understand the current demand for machine learning talent in the UK Leverage your organisational, communication and analytical skills in ML contexts Overcome common re-entry challenges with practical solutions Refresh your technical knowledge through targeted learning Access returnship and re-entry programmes tailored to machine learning Find roles that fit around family commitments—whether flexible, hybrid or full-time Balance your career relaunch with caring responsibilities Master applications, interviews and networking specific to ML Learn from inspiring returner success stories Get answers to common questions in our FAQ section Whether you aim to return as an ML engineer, research scientist, MLOps specialist or data scientist with an ML focus, this article will map out the steps and resources you need to reignite your machine learning career.

LinkedIn Profile Checklist for Machine Learning Jobs: 10 Tweaks to Drive Recruiter Interest

The machine learning landscape is rapidly evolving, with demand soaring for experts in modelling, algorithm tuning and data-driven insights. Recruiters hunt for candidates proficient in Python, TensorFlow, PyTorch and MLOps processes. A generic profile simply won’t cut it. Our step-by-step LinkedIn for machine learning jobs checklist covers 10 targeted tweaks to ensure your profile ranks in searches and communicates your technical impact. Whether launching your ML career or seeking leadership roles, these optimisations will sharpen your professional narrative and boost recruiter engagement.

Part-Time Study Routes That Lead to Machine Learning Jobs: Evening Courses, Bootcamps & Online Masters

Machine learning—a subset of artificial intelligence—enables computers to learn from data and improve over time without explicit programming. From predictive maintenance in manufacturing to recommendation engines in e-commerce and diagnostic tools in healthcare, machine learning (ML) underpins many of today’s most innovative applications. In the UK, demand for ML professionals—engineers, data scientists, research scientists and ML operations specialists—is growing rapidly, with roles projected to increase by over 50% in the next five years. However, many aspiring ML practitioners cannot step away from work or personal commitments for full-time study. Thankfully, a rich ecosystem of part-time learning pathways—Evening Courses, Intensive Bootcamps and Flexible Online Master’s Programmes—empowers you to learn machine learning while working. This comprehensive guide examines each route: foundational CPD units, immersive bootcamps, accredited online MSc programmes, funding options, planning strategies and a real-world case study. Whether you’re a software developer branching into ML, a statistician aiming to upskill, or a professional exploring AI-driven innovation, you’ll discover how to build in-demand ML expertise on your own schedule.