Lead Data Engineer

Amwins Global Risks
City of London
4 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer (AWS & Snowflake)

Senor / Lead Data Engineer

Lead Data Engineer
Amwins Global Risks | London | Permanent
The Opportunity

Amwins Global Risks is building their first dedicated Data Engineering team as part of an ambitious new Data Strategy. This is a rare chance to join a top 10 Lloyd's contributor at the ground floor of their data transformation journey.


This role is perfect for a hands-on technical leader who thrives in entrepreneurial environments - someone who wants to own and build a data platform from the ground up rather than inherit established systems.


The Role in Context

  • New team creation: Leading 4-5 Data Engineers in a greenfield environment
  • Low data maturity starting point: All the challenges and opportunities that come with building from scratch
  • High autonomy: You'll own the data platform and have the freedom to establish technical frameworks
  • Reporting to: Ben Sutton, Head of Reporting & Analytics (DataIQ Future Leaders 2025 recipient)
  • Technology stack: Azure (Data Factory, Data Lake, DevOps) + Databricks

Key Selling Points

Own the entire data platform - not just maintaining someone else's work


Define technical standards from the ground up


Small but mighty team - high impact, collaborative environment


Hands-on leadership - split between coding and team management


Established, stable company with global reach (800+ employees, 150+ countries)


Investment in growth - flat structure that values expertise and relationships


Ideal Candidate Profile
Must-haves

  • Hands-on coding appetite - this isn't a pure management role
  • Team leadership experience - comfortable managing and mentoring engineers
  • Advanced skills: SQL, Python, PySpark, Databricks, Azure stack
  • Enjoys building from scratch - thrives in low-maturity, high-potential environments

Deal-breakers

  • ❌ Candidates who've "moved away" from hands-on engineering
  • ❌ Those uncomfortable with people management responsibilities
  • ❌ Anyone seeking established, mature data environments

Target Markets & Approach
Sectors to explore

  • Financial Services (beyond insurance)
  • Don't limit to Lloyd's/London Market - we're open to strong engineers from any industry
  • Technology companies with strong data engineering practices
  • Scale-ups where candidates have built teams/platforms from scratch

Ideal background

  • Principal/Lead Data Engineers looking for more ownership
  • Senior Data Engineers ready to step into leadership
  • Technical leaders who've built data platforms in challenging environments

Logistics

  • Hybrid working: 2-3 days in London office (potentially more initially)
  • Start date: Flexible - new role allows for proper onboarding

Key Interview Themes

  1. Hands-on technical skills - expect coding discussions/challenges
  2. Leadership philosophy - how they'd build and mentor a new team
  3. Problem-solving in low-maturity environments - comfort with ambiguity
  4. Ownership mindset - examples of building systems from scratch


#J-18808-Ljbffr

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.

New Machine Learning Employers to Watch in 2026: UK and Global Companies Driving ML Innovation

Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services. For professionals exploring opportunities on www.MachineLearningJobs.co.uk , understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.