Analytics Engineer

We are looking for an analytics engineer to join our risk and reserving function and own the data foundations that power exposure management analytics and reserving workflows and lead the transition of our semantics layer from dbt Core + AWS to dbt Cloud + GCP, enabling consistent consumption via Looker and Power BI.

• Risk & reserving data modelling -Build and maintain curated datasets that represent policies, exposures, claims and reserving concepts in a consistent, analytics-ready way. This is done by designing clear, reusable models for exposure and reserving use-cases and maintaining metric definitions, segmentations, and business logic as a single source of truth.
• Pipeline ownership -Deliver reliable, performant dbt pipelines from source systems through to consumption layers. Develop, run, monitor and optimise transformations and schedules. Keep pipelines maintainable through clean structure, version control, code review, documentation.
• Data quality, controls, and reconciliation -Make the numbers trustworthy and explainable. Implement tests and checks (technical + business-rule validations). Own reconciliations to source systems / finance totals; investigate and resolve discrepancies.
• Enable exposure & reserving analytics -Translate analytical needs into data products that reduce manual effort for the team. Support reserving-ready structures (e.g., cohorts, development periods, triangles-ready extracts). Support exposure analytics (portfolio mix, trends, accumulations / monitoring views as required).
• Platform translation and migration (AWS GCP) -Understand the current estate and re-platform it safely. Map existing AWS / dbt Core logic and dependencies; define a sensible migration sequence. Implement dbt Cloud on GCP standards (environments, CI / testing, scheduling, documentation) and migrate priority models with parity checks.
• Collaborate with the broader data / tech team -Ensure Risk & Reserving requirements are understood and delivered by the platform owners. Gather and clarify requirements from Risk & Reserving; turn them into precise, testable asks. Liaise with the internal data / GCP team on ingestion, modelling patterns, access, performance, and governance and ensure solutions meet Risk & Reserving aims (definitions, controls, SLAs).
• Documentation and stakeholder management -Keep delivery clear, traceable, and easy to adopt, both downstream (with the internal risk and reserving team) and upstream with the broader tech / data engineering community.

Do you have experience in SQL?, * 3+ years writing SQL (complex transformations, performance-aware querying, strong data modelling instincts).

• Ideally 2+ years working with insurance / risk / reserving / actuarial data (or closely related experience with similar controls and reconciliation needs).
• Analytics engineering strength - Builds maintainable, reusable datasets that stakeholders trust and reuse. Strong grasp of modelling patterns (dimensions/facts, modular layers, metric consistency).
• dbt capability - Hands-on experience with dbt Core (models, tests, documentation; macros a plus). Comfortable adopting dbt Cloud ways of working (environments, scheduling, CI patterns).
• Tech / SQL experience - understands the strengths and limitations of AWS Redshift / GCP and cloud environments and knows how to leverage these to meet the needs of risk and reserving analysis.
• Quality and reconciliation mindset - Designs controls, tests assumptions, reconciles to sources, and can explain numbers under scrutiny. Confident investigating discrepancies and driving issues to resolution.
• Cross-team collaboration - Able to liaise effectively with the broader data/tech team to get requirements delivered properly. Can translate Risk & Reserving needs into clear, testable requirements and hold the quality bar.
• Delivery habits - Uses version control (Git), communicates clearly, documents logic, and works effectively across multiple stakeholders.

(Desirable)

• Reserving data experience (triangles/dev periods/AY-UY).
• Looker and/or Power BI experience (governed metrics, semantic consistency).
• Python for validation/automation; familiarity with data observability approaches.

This role will be based in our London office in a 50/50 Hybrid mode.

We match your pension contributions up to 7%

Private medical & Dental cover

Learning budget of £1,000 a year + Study leave (with encouragement to use it)

Enhanced maternity & paternity

Travel season ticket loan

️ Access to a wide selection of London O2 events and use of a Private Lounge

Employee Wellbeing Programme

Prayer room in Office

What We Stand for and Next Steps

"We pride ourselves on being an equal opportunity employer. We treat all applications equally and recruit based solely on an individual's skills, knowledge, and experience. The quality and growing diversity of our team is a testament to this commitment"

At Policy Expert, we are committed to fostering an inclusive and supportive environment for all candidates. If you require any reasonable adjustments during the interview process to accommodate your needs, please do not hesitate to let us know. We are dedicated to ensuring every candidate has an equal opportunity to succeed and will work with you to provide the necessary support.

Policy Expert is a forward-thinking business that loves to get things done. Leveraging proprietary technology and smart data, we offer reliable products and a wow customer experience. Having achieved rapid growth since being founded in 2011, we've won over 1.5 million customers in Home, Motor and Pet insurance and have been ranked the UK's No.1-rated home insurer by Review Centre since 2013. Hear from our team about what it's like working at Policy Expert