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

Apply Now

Pricing/Actuarial - Data Engineering Manager

Invecta
London
6 months ago
Applications closed

Related Jobs

View all jobs

R&D Senior Data Scientist

Pricing Data Science Lead- SME

Pricing Data Science Lead- SME

Pricing Data Science Manager

Pricing Data Science Lead- SME

Pricing Data Science Lead- SME

A leading insurance organisation is seeking aData Engineering Managerto play a pivotal role in a large-scale pricing and underwriting transformation programme. This opportunity is particularly well-suited toactuarial professionals with a strong technical/data backgroundwho are moving into data engineering, or experienced data engineers with exposure to the commercial insurance sector.


This is a high-impact role within a central data function supporting pricing, underwriting, and broader commercial decision-making - ideal for someone who understands both the technical and strategic value of data in an insurance context.


Key Responsibilities:

  • Lead the design and build of a centralised data analytics capability to support technical underwriting and pricing.
  • Collaborate with data science and programme teams to deliver robust, accurate, and actionable insights.
  • Oversee the development of ETL pipelines, reporting frameworks, and a new underwriting datamart built on Databricks.
  • Champion data engineering best practices, automation, and cloud adoption across the function.
  • Work closely with senior stakeholders to shape strategy and deliver value from data initiatives.


Ideal Candidate Profile:

  • Actuarial background with a focus on data and transformation, or strong data engineering experience within the insurance sector.
  • Advanced programming skills inPythonandSQL.
  • Experience withDatabricks, cloud platforms, and CI/CD pipelines is highly desirable.
  • Proven ability to manage teams and/or complex projects with a strong focus on stakeholder engagement.
  • Exceptional communication and analytical skills, with the ability to simplify complexity and drive meaningful outcomes.

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.

Jobs

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Jobs

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Jobs

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.