Inigo
Data Engineer
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About Inigo
We enable your ambition. We insure good people – knowledgably, fairly, and efficiently. We combine our expertise with the best of science, data, and analytics.
Inigo’s aim is to create an underwriting-focused insurance and reinsurance business, concentrating on limited classes of business and build a strong reputation in the Lloyd’s market. To achieve our strategic objectives, we want to build a clean tech enabled platform with leading technological capabilities and attract a team of talented, high-quality people. At the top of our agenda is to create a diverse and open culture where we foster talent and provide opportunities to build a rewarding career in a company that is vibrant and at the start of an exciting journey.
We have a culture that is inclusive, fun, and constantly strives for excellence, we describe it as ‘all in’. Our values are:
- Get Smart – we ask questions, explore, learn, and continuously strive for excellence
- Park the Ego – we are welcoming and open, and embrace different thinking
- Share the Passion – we collaborate and communicate our expertise honestly and thoughtfully
- Radical Simplicity – we are transparent, focused, and actively avoid complexity in how we operate
About the Team
The Data team was established to build the central Data Platform that underpins Inigo's insights, pricing, and risk selection tools. The team brings together data engineering, data governance, data analysts, and data testers, and works closely with data scientists and actuaries across the business.
The Inigo Data Platform is built in Azure and designed around cloud-native tooling. Working for a technology and data focused Lloyd's insurer means you'll see the impact of your work quickly — the platform is small enough to move fast and important enough that what you build gets used.
Inigo is serious about using AI well. Models applied to underwriting, pricing, and risk selection are only as good as the data underneath them, and none of it works without well-modelled, well-governed, trustworthy datasets. That's what this team builds.
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We're excited about what the platform can do for Inigo, and we're looking for someone to help us deliver it.
About the Role
The Data Engineer will work within the Data Engineering team to build and run data pipelines in an Azure cloud-native environment. The platform is built on Azure Databricks, with Azure Data Factory handling orchestration. The curated datasets you build are consumed by the business through Power BI or directly in Databricks, so you'll be modelling data for analysts and actuaries to use.
This role is about delivery. You'll take ownership of pipelines end to end — from ingestion through to the curated datasets business users depend on — working within the patterns and framework the team has already established. Strong Python, SQL, and Databricks skills are the core requirement. There’s plenty of scope to grow into platform design work over time.
The role will suit someone who communicates clearly, asks good questions of the business, and enjoys working as part of a team.
The successful candidate will do this by:
- Building, testing, and maintaining data pipelines in Databricks and Python, following the team's established patterns and standards
- Orchestrating and scheduling those pipelines in Azure Data Factory
- Modelling curated datasets so they're straightforward for analysts to consume — designing fact and dimension tables, agreeing grain, and handling history
- Writing and optimizing SQL against Delta Lake and the wider platform
- Ingesting data from a range of sources — files, APIs, and databases — and handling the practicalities of incremental loads, late-arriving data, and change tracking
- Deploying your work safely to production using the team's CI/CD pipelines, with automated tests
- Monitoring pipelines in production, investigating failures, and fixing them
- Working with analysts, data scientists, actuaries, and non-technical stakeholders to understand business problems and translate into well-designed solutions
- Contributing to code reviews and helping keep the codebase and documentation in good shape
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To do this, we’re going to need you to bring:
Essential
- Experience building data pipelines in Python — comfortable writing clean, testable code
- Strong SQL, including query optimization and an understanding of how data is modelled and stored
- Practical experience of dimensional modelling — star schemas, facts, and dimensions, choosing grain
- Hands-on experience with Spark, ideally PySpark on Databricks
- Experience building and scheduling pipelines in Azure Data Factory, or a comparable orchestration tool
- Incremental ingestion patterns — watermarking, SCD Type 2, soft deletes
- Integrating with third-party REST APIs — token auth, pagination, rate limits
- Working knowledge of core Azure data services, for example ADLS Gen2 and Azure SQL Database
- Familiarity with Git and CI/CD — you've had your code reviewed, merged, and deployed through a pipeline
- An understanding of ETL/ELT patterns and why pipelines are built the way they are
- The ability to take a requirement, ask the right clarifying questions, and deliver a working solution
- A willingness to learn our stack and our domain, and to ask for help when you're stuck
Nice to have
- Delta Lake and medallion (bronze/silver/gold, raw/base/curated) architecture
- Building datasets that feed Power BI or similar, and an understanding of how your modelling decisions land downstream
- Azure DevOps pipelines, and SQL database deployments via DACPAC or SQL projects
- Automated testing of data pipelines with Pytest
- Azure Functions, Service Bus for event-driven workloads
- Infrastructure as code, for example Terraform
- Experience in Lloyd's of London, general insurance, or wider financial services
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