Data Engineering Lead

Overview

Willis Re is building its global technology estate from the ground up, unencumbered by legacy and designed around data, analytics and modern cloud platforms. Our Snowflake data lake platform sits at the centre of that estate, and we are looking for a Data Engineering Lead to drive its implementation.

About Willis Re:
We combine specialist broking with analytics, modeling and research to help insurers optimize risk transfer, strengthen balance sheets and achieve sustainable growth. Our approach is relationship-driven, transparent and outcome-focused. At the heart of Willis Re is a focus on delivering the most cutting-edge analytical solutions to enable more informed, better decision-making for risk selection, portfolio optimization and capital management. The launch of Willis Re brings a strategic advantage of being unhindered by legacy, an ability to leverage data, statistical models and advanced technologies with the best knowledge and expertise to deliver more efficient and effective reinsurance outcomes.

This places Willis Re in a unique position to build a truly analytically driven business, focused on creating solutions for the reinsurance industry that are future‑led and forward‑thinking. Willis Re will also leverage recognized technical expertise from WTW’s Insurance Consulting & Technology business including their advanced modelling and analytical capabilities. Alongside this will be WTW’s Research Network, an award‑winning business supporting and influencing science to improve the understanding and quantification of risk.

• Snowflake Development:
  Spend the majority of your time hands‑on in Snowflake, architecting and building the platform and its surrounding ecosystem, including ingestion, transformation, data models, curated data products, and warehouse, performance and cost design for high‑volume reinsurance placement, exposure, claims and market data.
• Tagging & Cataloguing:
  Establish and maintain data tagging, classification and cataloguing, so that data across the platform is discoverable, well described and correctly labelled for sensitivity and business meaning.
• Data Governance:
  Own governance for the platform, covering ownership and stewardship, lineage, access policies, retention obligations and cross‑border data residency, working with security, risk and the business.
• Data Quality:
  Define and implement data quality controls, automated testing, monitoring and reconciliation, and make quality visible and measurable to data consumers.
• Scale & Archival:
  Design the platform to handle growing data volumes predictably, defining partitioning and clustering, storage tiering, retention and archival strategy, and keeping performance and cost under control as the estate grows.
• Architecture Contribution:
  Contribute to the data architecture, working with the Solution Architect and Head of Architecture & Engineering to shape target‑state designs and feed real‑world constraints back into them.
• Vendor Leadership:
  Direct and quality‑assure the work of strategic delivery partners, reviewing their designs and code and holding them to the agreed standards.
• Engineering Standards:
  Set how the team builds, covering CI/CD for data, automated testing, observability and infrastructure‑as‑code.
• Grow the Team:
  Mentor engineers, raise the technical bar, and help shape how the data engineering function scales.

• 10+ years in data engineering, including experience leading the delivery of a significant data platform end‑to‑end.
• Deep, current, hands‑on Snowflake expertise across the platform and its ecosystem, including warehouse sizing, performance and cost optimisation, RBAC and access design, object tagging and masking policies, ingestion (Snowpipe, Streams and Tasks), sharing and marketplace, AI and ML capabilities such as Cortex, and transformation and orchestration tooling such as dbt.
• Strong track record in data governance, covering tagging, classification, data catalogues, lineage, stewardship and access policies.
• Practical experience implementing data quality frameworks, automated testing and monitoring, and driving measurable improvement.
• Deep expertise in data modelling…