Data Engineer (London Market)

Job Title: Data Warehouse Engineer (SQL / Azure)

Location: Central London, 80 Fenchurch Street, 2 days per week

Contract: 6 Months

Key Responsibilities

• Design, build, and optimise SQL-based data warehouse solutions supporting underwriting, claims, actuarial, exposure management, and regulatory reporting.
• Develop robust ETL/ELT pipelines using tools such as SSIS, Azure Data Factory (ADF), and Databricks.
• Implement dimensional modelling (Kimball / star schema) for analytical workloads.
• Integrate data from internal and external London Market sources including Lloyd’s, DXC, TPAs, and brokers.
• Build automated data quality checks, lineage tracking, and controlled data releases.
• Collaborate with Product Owners, Underwriters, Actuaries, BI teams, and IT to translate business requirements into technical specifications.
• Support migration of legacy SQL systems into Azure cloud environments, enabling self-service analytics for Power BI and actuarial platforms.

Candidate Profile

Technical Skills:

• Data warehouse design, dimensional modelling, and star schema expertise.
• Experience with ETL/ELT tools (SSIS, ADF, Databricks).
• Knowledge of Azure cloud data stack (SQL Pool, Synapse, Data Lake, ADF).
• Familiarity with APIs, REST, JSON, and flat-file ingestion.
• Performance tuning and optimisation for large-scale data warehouses.

London Market / Insurance Knowledge:

• Policy, claims, and finance datasets, including Lloyd’s submissions & reporting.
• Understanding of underwriting and actuarial processes.

Soft Skills:

• Excellent communication with technical and non-technical stakeholders.
• Strong problem-solving mindset with attention to detail.
• Experience working in Agile / Scrum environments.
• Previous experience with London Market insurers, MGAs, Lloyd’s syndicates, or brokers.
• Data warehouse dimensional modelling and ETL/ELT development.
• Data Quality Assessment and embedding automated checks with lineage and reconciliation.
• Python or PySpark for data transformation.
• Exposure to BI Tools (Power BI).
• Experience with CI/CD, DevOps, and automated data pipelines.
• Master data management experience and orchestration frameworks (Airflow/ADF).

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