Data Engineer

Howden Group Holdings
Tunbridge Wells
1 month ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Who are we?

Howden is a global insurance group with employee ownership at its heart. Together, we have pushed the boundaries of insurance. We are united by a shared passion and no-limits mindset, and our strength lies in our ability to collaborate as a powerful international team comprised of 23,000 employees spanning over 56 countries.


People join Howden for many different reasons, but they stay for the same one: our culture. It’s what sets us apart, and the reason our employees have been turning down headhunters for years. Whatever your priorities – work / life balance, career progression, sustainability, volunteering – you’ll find like-minded people driving change at Howden.


Role responsibilities

  • Key member of the team, engineering DUAL Personal Lines’ strategic data platforms, chief of which is the Data Lakehouse, to enable the continuous enhancement of DUAL Personal Lines’ underwriting analytical capabilities.
  • Provide technical expertise in data engineering, analysis, orchestrations, and enrichment.
  • Build data solutions where required to address changing requirements – managing new requests and incidents.
  • Building, testing and deployment of data products while remaining true to governance principles including change control, accountability, data quality, and data security.
  • Consult the DUAL Group data team’s business analysts, developers and system owners to avoid divergence from agreed data standards.
  • Assesses the impact of change on DUAL Personal Lines’ data model to determine and avoid any potential issues which may hinder our operational, analytical and reporting environments.
  • Actively collaborates with data teams and system owners to maintain the data model and data integrity throughout the DUAL Group data lakehouse.
  • Closely support underwriting analytical teams and reporting analysts to ensure data is accurately represented for use in underwriting and performance analysis and reporting.
  • Build knowledge and expertise of DUAL Personal Lines’ systems with other data team members.
  • Working with other members of the data team and wider business to support delivery of additional project components (API interfaces, Data Models, Data Sharing, RDM/MDM tools, ML Endpoints).
  • Working within an Agile delivery / DevOps methodology to incremental change to the overall data platform to deliver additional data products to provide value to stakeholders.
  • Working with the Group Data Science team to productionise ML pipelines into the overall data platform solution.
  • Work collaboratively with internal stakeholders across DUAL Personal Lines including but not limited to Underwriting teams, Operations, Finance, Risk & Compliance and HR.
  • Act as a conduit between DUAL Personal Lines and Dual Data Team to facilitate the sharing of common approaches, adherence to standards, and advocating on behalf DUAL.
  • Help drive success of future investments in Data within DUAL Personal Lines, including clear articulation of return on investment (ROI) and ongoing assessment of realisation attainment.

Key requirements

  • Strong knowledge of Data Management principles in a Lakehouse architecture.
  • At least 5 years’ experience in data engineering and building data pipelines.
  • Proven track record in Data Engineering and supporting the business to gain true insight from data. Experience in data integration and modelling including ELT pipelines.
  • Hands on experience designing and delivering solutions using Azure services including Azure Data Factory, Azure Databricks, Azure Storage, Azure DevOps.
  • Strong proficiency in Python and SQL
  • Experience working with Data Architects for technical design
  • Experience on the design of data models (Star Schema) for use in Power BI. Insurance analysis, MI or reporting experience
  • Understanding and adherence to CI/CD principles
  • Nice to have: experience working alongside Data Science teams to assist with building and deploying Machine Learning models
  • Nice to have: experience working with infrastructure as code tools for deploying resources
  • Ability to work quickly, efficiently and methodically.
  • A strong team player who is confident in their ability.
  • Very strong communication, influencing and negotiation skills. Actively listens to the views of colleagues, but also has the strength of character to challenge where required.
  • Has a commercial awareness and stays up to date with current issues affecting the industry and its technologies.
  • Proactively sharing best practice with others across the organisation.
  • Planning, organising, and managing skills, and ability to prioritise.
  • Good understanding of data operations.
  • Broad knowledge and understanding of insurance principles, products and services.
  • Self-starter, with a passion and ability for learning new skills and technologies

What do we offer in return?

  • A career that you define. At Howden, we value diversity – there is no one Howden type. Instead, we’re looking for individuals who share the same values as us:
  • Our successes have all come from someone brave enough to try something new
  • We support each other in the small everyday moments and the bigger challenges
  • We are determined to make a positive difference at work and beyond

Reasonable adjustments

We're committed to providing reasonable accommodations at Howden to ensure that our positions align well with your needs. Besides the usual adjustments such as software, IT, and office setups, we can also accommodate other changes such as flexible hours* or hybrid working*.


If you're excited by this role but have some doubts about whether it’s the right fit for you, send us your application – if your profile fits the role’s criteria, we will be in touch to assist in helping to get you set up with any reasonable adjustments you may require.


*Not all positions can accommodate changes to working hours or locations. Reach out to your Recruitment Partner if you want to know more.


Permanent


#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.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.