Head of Data Engineering

Data is at the core of SCOR’s strategic plan Forward 2026 as one of its key enablers. The Chief Data Officer organization plays a central role in delivering a governed, trusted data platform acting as a Single Version of Truth across the organization. 

As Head of Data Engineering, you are accountable for the end-to-end data integration, ensuring that data is correctly extracted, transformed, and delivered from operational systems into analytical and AI platforms.

Your primary responsibility is to design and operate scalable data pipelines (batch, real-time, streaming) that ensure consistent, high-quality data flows across the enterprise. You ensure strong alignment between business logic, technical implementation, and system interoperability, enabling reliable and governed data delivery.

A key part of the role is to work upstream with business and IT teams to improve data quality at source, embedding controls, ownership, and governance into operational processes and systems.

You lead cross-functional engineering teams across the Property & Casualty, Life & Health, Finance, and Risk domain, and operate in an agile, product-based delivery model, ensuring that data integration capabilities continuously evolve to meet business needs. In parallel, you drive the evolution of data engineering practices by integrating agentic AI capabilities, automating pipeline development, monitoring, and optimization to increase delivery velocity and reliability.

Key duties and responsibilities

• Design and implement robust, scalable, and reusable data integration pipelines (batch, real-time, streaming) to serve enterprise analytics and AI use cases
• Drive engineering excellence by enforcing best practices to ensure reliable, scalable, and high-performance data pipelines
• Bridge business logic, technical semantics, and system interoperability to enable consistent and governed data delivery
• Provision clean, structured, and contextualized data to support analytics products, dashboards, and AI initiatives
• Ensure alignment with analytical data models, semantic layers, and data product definitions across domains
• Collaborate with business and IT teams to identify and resolve data quality issues at source and embed data quality requirements into system evolution
• Translate business and analytical requirements into scalable technical integration designs
• Define and enforce best practices, frameworks, and standards for data engineering and integration
• Ensure high performance and scalability of pipelines through monitoring of latency, data freshness, and reliability
• Define and track KPIs and SLAs (e.g. uptime, delivery performance) to ensure operational excellence
• Lead and develop cross-functional engineering teams, driving agile delivery and clear prioritization aligned to business impact
• Drive the transformation of data engineering by leveraging agentic AI, enabling AI-assisted pipeline development and testing and alike