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

Nominate & Attend

Data Engineer - Insurance - Didcot

Noir
Didcot
1 month ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer - Insurance Company - Didcot


(Tech Stack: Data Engineer, Databricks, Python, Power BI, AWS QuickSight, AWS, TSQL, ETL, Agile Methodologies)


We’re working in partnership with a forward-thinkinginsurance company based in Didcotthat’s heavily investing in data and technology to drive smarter decisions and better outcomes for their customers. As part of this growth, they are now seeking aData Engineerto help shape their data infrastructure and analytics capabilities.


The Role

As a Data Engineer, you’ll play a crucial role in designing and maintaining data pipelines, ensuring data is clean, structured, and accessible for analysis. You'll work with modern data tools and cloud technologies to enable real-time insights and support strategic business decisions.


Key Responsibilities

  • Develop and maintain scalable data pipelines and solutions for data ingestion, transformation, and delivery.
  • UsePythonto automate data workflows, support ETL processes, and enhance analytics capabilities.
  • Work withrelational databasessuch asMySQL, Redshift,andPostgreSQLto manage and model structured data.
  • LeverageAWS cloud servicesincludingS3, Redshift, Athena,andLambdato support data storage, querying, and processing.
  • Support business reporting and analytics through integration withBI toolslikeLooker, Power BI, orTableau.
  • Collaborate usingversion control toolssuch asGit, following best practices for code reviews and collaborative development.
  • Work alongside digital teams to implement and analyzeweb and behavioural analytics, using platforms likeGoogle Analytics, Matomo, orserver-side trackingtools.


What We're Looking For

  • Solid experience withSQLand a strong grasp ofdata modellingconcepts.
  • Proficiency inPython, particularly for data transformation, automation, and analytics.
  • Hands-on exposure toAWS data services(S3, Redshift, Athena, Lambda) is a strong advantage.
  • Understanding ofBI/reporting toolsand how to support business intelligence needs.
  • Familiarity withversion control systems(Git) and collaborative engineering practices.
  • Interest or experience inweb analyticsand user behavior tracking tools.


Why Join?

  • Join a stable and growing company in the insurance sector
  • Work in a collaborative team with real impact on decision-making
  • Hybrid working model offering flexibility and work-life balance
  • Competitive salary and benefits package
  • Opportunity to work with cloud-native and modern data technologies


Location:London/Remote Working UK


Salary:£45,000 – £55,000 + Bonus + Pension + Benefits


Applicants must be based in the UK and have the right to work in the UK even though remote work is available.


To apply for this position please send your CV to Matt Jones at Noir.


NOIRUKTECHREC


NOIRUKREC


NC/RG/DE

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.