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

Apply Now

Head of Data Engineering

Harnham
Kent
2 days ago
Create job alert

Head of Data Engineering

Hybrid - Kent (3 Days per Week)

Up to £130,000 + 30% Bonus + Benefits

Are you an experienced data leader ready to take ownership of a large-scale data transformation? We’re working with a leading UK financial services group that’s on a major journey to modernise its data estate — and they’re looking for a Head of Data Engineering to lead the migration from on-premise to a modern, Azure-based cloud platform.

This is a high-impact leadership role, ideal for someone who combines strategic vision with hands-on technical credibility and thrives on building, scaling, and delivering enterprise-grade data solutions.

💡 Why this role?

  • Lead the migration of legacy on-premise systems to Azure , driving one of the organisation’s most strategic technology programmes.
  • Shape and execute the data engineering roadmap , delivering scalable, secure, and compliant data solutions.
  • Build and mentor a high-performing engineering team , embedding modern cloud-first practices.
  • Hybrid flexibility: 3 days per week in the Kent office , with the rest remote.
  • Excellent package: up to £130,000 + 30% bonus and comprehensive benefits.

👩 💻 What you’ll be doing:

  • Leading the end-to-end migration from on-prem to Azure , defining architecture, governance, and delivery best practices.
  • Designing, developing, and optimising data pipelines, integration frameworks, and ETL processes in Azure.
  • Building and maintaining scalable solutions using Azure Data Lake, Synapse, Data Factory, and Databricks .
  • Establishing robust data governance, quality, and lineage frameworks across all environments.
  • Collaborating closely with data architecture, analytics, and IT to ensure a seamless transition and platform stability.
  • Managing delivery across multiple workstreams, ensuring projects are on time, within budget, and high quality.
  • Developing team capability — coaching engineers and fostering a culture of innovation and ownership.
  • Presenting updates and strategic insights to senior leadership and executive stakeholders .

🎯 What we’re looking for:

  • Proven track record leading data engineering teams through large-scale transformations.
  • Strong, hands-on understanding of Azure data services — e.g. Synapse, Data Factory, Data Lake, Databricks, Purview .
  • Direct experience migrating from on-premise environments to Azure , ideally within a regulated or financial services context.
  • Deep technical knowledge of data architecture, ETL/ELT, and data modelling .
  • Excellent coding skills in SQL and Python , with experience implementing DevOps for data and CI/CD.
  • Strong leadership and stakeholder engagement — able to translate technical progress into business outcomes.
  • Strategic thinker with a delivery mindset and the ability to influence at senior levels.

Nice-to-haves:

  • Experience with Snowflake or hybrid multi-cloud data solutions.
  • Familiarity with banking or financial data frameworks such as BCBS239 or IRB.
  • Background in agile delivery and infrastructure automation using Terraform or similar.

💰 Package & Benefits:

  • Salary up to £130,000 + 30% annual bonus .
  • Hybrid working – 3 days per week in Kent office .
  • Private medical insurance, life assurance, and pension scheme.

Related Jobs

View all jobs

Head of Data Engineering

Head of Data Engineering

Head of Data Engineering and Architecture

Head of Data Engineering

Head of Data Engineering

Head of Data Engineering

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.

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.

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.

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.