Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Design and build scalable data pipelines for analytics and reporting.
- Company: Join HDI, a global leader in insurance with over 120 years of experience.
- Benefits: Competitive salary, inclusive culture, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on innovation and career development.
- Why this job: Be part of a data transformation journey that drives impactful decision-making.
- Qualifications: Expertise in SQL, data modelling, and cloud platforms like Azure.
The predicted salary is between 50000 - 70000 £ per year.
About us HDI is a Corporate & Specialty Insurer part of the Talanx Group. With over 120 years of experience, HDI operates across five continents, around 40 countries and employs over 5,000 people worldwide.
The Data Engineer is responsible for designing, building, and maintaining robust, scalable data pipelines and cloud‑based data infrastructure to support analytics, reporting, data modelling, underwriting insights, and regulatory needs across HDI UK&I. This role ensures timely, trusted, well‑structured data delivery into appropriate data marts & warehouses, and downstream data feeds which are used by Actuarial, Finance, Operations, and Underwriting. The position forms a core part of HDI’s data transformation agenda, enabling improved decision‑making, automation, and analytics maturity.
Key accountabilities
- Design, develop, and maintain end to end ingestion pipelines into appropriate data technologies such as Snowflake from internal systems (policy admin, claims, finance) and external data sources.
- Build orchestrated ELT/ETL processes using modern tooling and best practice engineering patterns.
- Implement incremental refresh, schema evolution management, and data validation tests.
- Ensure data availability aligned to business SLAs (e.g., daily refresh for actuarial & finance repositories).
Data Modelling & Warehouse Development
- Create well‑structured dimensional and relational data models for analytical use cases.
- Develop canonical, reusable datasets (curated marts) for Analytics, Actuarial, and Finance.
- Own the technical modelling layer in Snowflake including schema design, performance optimisation, cost control, and warehouse governance.
- Collaborate closely with Analytics Engineers using dbt, ensuring transformations are production‑grade, tested, and fully documented.
Data Quality, Testing & Governance
- Implement automated testing suites, data contracts, lineage, and monitoring frameworks.
- Partner with Data Governance to embed quality rules, SLAs, and metadata standards into pipelines.
- Resolve data quality issues proactively and own improvements to source‑to‑target data flows.
Cross‑Functional Collaboration
- Work with business areas as needed to supply structured data sets for relevant business processes.
- Drive the building of automated, trusted data feeds for analytics requirements.
- Partner with Data Analysts to accelerate dashboarding and advanced analytics.
- Collaborate with Technology teams to ensure secure, reliable platform operation.
- Optimise Snowflake/SQL/Python query performance, warehouse sizing, storage costs, and compute efficiency.
- Implement workload separation, time travel optimisation, clustering, and pruning strategies.
Documentation & Knowledge Sharing
- Produce comprehensive documentation for pipelines, data models, data flows, and architecture components.
- Provide technical guidance to junior team members and evangelise engineering best practices.
Skills & experience
Technical Skills
- Expert SQL engineering capability.
- Advanced experience with schemas, warehouses, stages, tasks, streams, performance tuning.
- Experience of modern transformation frameworks (Snowflake/DBT preferred - but not essential).
- Python for scripting, automation, and orchestration.
- Experience with CICD pipelines (GitHub Actions / Azure DevOps), code reviews, and versioning.
- Strong understanding of data modelling, data warehousing patterns, and ELT best practice.
- Familiarity with PowerBI or BI model structures to support downstream analytics.
- Cloud platform experience (Azure preferred).
Business & Domain Skills
- Prior experience in insurance, especially commercial/specialty lines, claims, actuarial or finance data structures.
- Understanding of regulatory expectations around data quality, lineage, and auditability (desired but not essential).
Professional
- Degree in Computer Science, Engineering, Mathematics or similar (or equivalent professional experience).
- dbt certification beneficial.
- Snowflake certifications advantageous.
As an equal opportunities employer, we are committed to creating an inclusive environment for all employees, recognising that a diverse and inclusive workplace is a creative and prosperous one. If you require support with your application, please contact UK&IRE_Recruitment@hdi.global.
Data Engineer employer: HDI
Contact Detail:
HDI Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at HDI or similar companies. A friendly chat can open doors and give you insights that job descriptions just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data engineering projects. Whether it's a cool data pipeline or a slick dashboard, having something tangible can really impress hiring managers.
✨Tip Number 3
Prepare for the interview like it’s the big game! Research HDI's data transformation agenda and think about how your experience aligns with their needs. Be ready to discuss specific examples of your work.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining the team.
We think you need these skills to ace Data Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Data Engineer role. Highlight your experience with SQL, data pipelines, and any relevant tools like Snowflake or dbt. We want to see how your skills match what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about data engineering and how you can contribute to our team at HDI. Keep it concise but impactful – we love a good story!
Showcase Your Projects: If you've worked on any cool data projects, don’t forget to mention them! Whether it's a personal project or something from a previous job, we want to see your hands-on experience and creativity in action.
Apply Through Our Website: We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to keep track of your application status. Plus, we love seeing applications come directly from our site!
How to prepare for a job interview at HDI
✨Know Your Data Tools
Make sure you brush up on your SQL skills and get familiar with Snowflake and dbt. Be ready to discuss how you've used these tools in past projects, as well as any challenges you faced and how you overcame them.
✨Showcase Your Problem-Solving Skills
Prepare examples of how you've designed and maintained data pipelines or tackled data quality issues. Highlight your thought process and the impact of your solutions on previous teams or projects.
✨Understand the Business Context
Familiarise yourself with the insurance industry, especially commercial and specialty lines. Knowing how data impacts underwriting and finance will help you connect your technical skills to the business needs of HDI.
✨Be Ready for Technical Questions
Expect questions about data modelling, ELT best practices, and performance tuning. Practise explaining complex concepts in simple terms, as you'll need to collaborate with various teams across the organisation.