Casualty Analytics Data Engineer

DataAnalystJobs; Greater London; Hybrid; GBP 60,000 - 90,000; Full time

Benefits offered by this job

Competitive compensation

Comprehensive benefits

Career development

Collaborative culture

Global reach

Job summary

Gallagher Re in London seeks a Data Analyst/Engineer to join the Casualty Analytics team. You will own data solutions, build pipelines, and collaborate with actuaries to support reinsurance placements. Hybrid work with three in-office days per week is expected.

The ideal candidate will have strong Python/pandas experience, robust SQL skills and a track record handling large, messy datasets to enable analysis-ready insights. GDPR knowledge is a plus.

Qualifications

• Bachelor’s degree in data science or related quantitative field.
• Proven production-quality Python experience with pandas.
• Strong SQL and data modelling skills for relational DBs.
• Experience with Git and collaborative development workflows.

Responsibilities

• Collaborate with brokers and clients to understand data requirements.
• Analyse large datasets to derive market trends and KPIs.
• Design, build and optimise Python data pipelines for ingest/transform/cleanse.
• Develop and implement robust data quality checks and controls.
• Lead the development of a central casualty exposure and claims database.
• Create analytical tools, dashboards and benchmarking datasets across portfolios.
• Stay ahead of trends in data analysis, visualization, data engineering and AI.
• Identify opportunities to automate manual processes.

Skills

Python

Pandas

SQL

Git

Excel

Power BI

Power Query

Data governance

Communication

Problem solving

Critical thinking

Education

Bachelor's degree (data science / related)

Tools

VS Code

Git

SQL databases

Power BI

Excel

Power Query

Python (pandas)

Job description

Gallagher Re in London seeks a Data Analyst/Engineer to join the Casualty Analytics team. You will own data solutions, build pipelines, and collaborate with actuaries to support reinsurance placements. Hybrid work with three in-office days per week is expected.

The ideal candidate will have strong Python/pandas experience, robust SQL skills and a track record handling large, messy datasets to enable analysis-ready insights. GDPR knowledge is a plus.