Data Engineer

Avencia Consulting
London, United Kingdom
Today
£40,000 – £60,000 pa

Salary

£40,000 – £60,000 pa

Job Type
Permanent
Work Pattern
Full-time
Work Location
On-site
Seniority
Mid
Education
Degree
Posted
30 Apr 2026 (Today)

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 role

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

Data Pipeline Engineering & Integration

  • 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.

Performance, Optimisation & Cost Efficiency

  • 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 ofdata modelling, data warehousing patterns, and ELT best practice.
  • Familiarity withPowerBI or BI model structures to support downstream analytics.
  • Cloud platform experience (Azure preferred).

Business & Domain Skills

  • Prior experience ininsurance, 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.

Other

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&

Related Jobs

View all jobs

Data Engineer

Noir Switzerland, United Kingdom
£87,291 – £113,478 pa Hybrid

Data Engineer

Lynx Recruitment London, United Kingdom
£40,000 – £85,000 pa On-site

Data Engineer

Gleeson Recruitment Group Birmingham, United Kingdom
£65,000 – £75,000 pa On-site

Data Engineer

Sanderson Cardiff, Cymru / Wales, CF10 2AF, United Kingdom
£60,000 – £72,000 pa Hybrid

Data Engineer

hireful Exeter, United Kingdom
£50,000 – £55,000 pa Hybrid

Data Engineer

Robert Walters Manchester, United Kingdom
£55,000 – £60,000 pa Hybrid

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.

Where to Advertise Data Engineering Jobs in the UK (2026 Guide)

Advertising data engineering jobs in the UK requires a different approach to most technical hiring. Data engineers occupy a distinct discipline that sits between software engineering, data science and cloud infrastructure — and the strongest candidates identify firmly with the data engineering community rather than with adjacent roles. General job boards consistently conflate data engineering with data analysis, data science and BI development, producing high application volumes but low candidate quality for specialist pipeline and platform roles. This guide, published by DataEngineeringJobs.co.uk, covers where to advertise data engineering roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

New Data Engineering Employers to Watch in 2026: UK and Global Companies Driving the Data Revolution

Data engineering is at the heart of the digital economy, transforming raw data into actionable insights, powering analytics, AI systems, and cloud infrastructure. As the UK and global markets continue to invest heavily in data platforms, pipelines, and real-time analytics, demand for skilled data engineers is growing rapidly. For professionals exploring opportunities on www.DataEngineeringJobs.co.uk , the critical question is: which companies are expanding, hiring, and shaping the future of data-driven business? This article highlights new data engineering employers to watch in 2026, including UK startups, scale-ups, and international firms expanding in the UK.

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

If you’re aiming for a career in data engineering, it can feel like you’re staring at a never-ending list of tools and technologies — SQL, Python, Spark, Kafka, Airflow, dbt, Snowflake, Redshift, Terraform, Kubernetes, and the list goes on. Scroll job boards and LinkedIn, and it’s easy to conclude that unless you have experience with every modern tool in the data stack, you won’t even get a callback. Here’s the honest truth most data engineering hiring managers will quietly agree with: 👉 They don’t hire you because you know every tool — they hire you because you can solve real data problems with the tools you know. Tools matter. But only in service of outcomes. Jobs are won by candidates who know why a technology is used, when to use it, and how to explain their decisions. So how many data engineering tools do you actually need to know to get a job? For most job seekers, the answer is far fewer than you think — but you do need them in the right combination and order. This article breaks down what employers really expect, which tools are core, which are role-specific, and how to focus your learning so you look capable and employable rather than overwhelmed.