Catastrophe Risk Developer/Data Engineer...

Avencia Consulting

London

1 day ago

Create job alert

Job Description

We're working with a leading global specialty insurer to recruit a data engineer into a high-profile analytics function supporting underwriting, portfolio management, and risk strategy across international markets.

This is a genuinely embedded role within the underwriting business - not a back-office data function. You'll sit at the intersection of data engineering, advanced analytics, and insurance risk, helping to modernise platforms, automate workflows, and deliver best-in-class exposure and catastrophe insights.

If you enjoy owning technical solutions end-to-end, influencing senior stakeholders, and working with complex insurance data, this role offers real scope and autonomy.

The Role

As part of a multi-disciplinary analytics team, you'll take technical ownership of data engineering and analytical workflows that underpin exposure management and catastrophe analytics.

Key Responsibilities

Designing and enhancing data pipelines and analytical workflows to improve efficiency, insight, and scalability
Leading development initiatives to modernise legacy processes, improve methodologies, and enhance documentation
Supporting platform upgrades, model changes, and troubleshooting complex technical issues
Building automated tools using a modern data stack, with a focus on clean data, performance, and reusability
Exploring opportunities to integrate AI and advanced automation into analytical workflows
Partnering closely with underwriting, exposure management, and senior analytics leadership to shape the technical roadmap
Acting as a technical mentor and raising data engineering capability across the team

What We're Looking For

This role will suit someone from the insurance or reinsurance market with strong technical depth and a pragmatic, delivery-focused mindset.

Essential skills & experience:
Strong programming capability in SQL (T-SQL / Dynamic SQL) and Python
Proven data engineering experience, including ETL design, data cleansing, and transformation of complex exposure data
Experience with text mining, regex, and unstructured data
Hands-on database design, optimisation, and ongoing maintenance
Experience integrating data via REST and SOAP APIs (XML, JSON, HTML)
Comfortable managing multiple workstreams and engaging confidently with stakeholders

Desirable (but not essential):
Experience working with catastrophe or exposure data
Familiarity with industry-standard catastrophe models (e.g. Verisk)

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Jan 18, 2026

Careers Jobs

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

Jan 13, 2026

Jobs

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Jan 6, 2026

Education Jobs

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.