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

Required experience & competencies

• Extensive hands-on experience in data engineering
• 4+ years in a leadership role, with proven ability to lead engineering teams or large cross functional initiatives and influence senior stakeholders
• Demonstrates an entrepreneurial mindset with a strong sense of ownership, taking accountability for delivering high impact data engineering solutions end to end
• Strong track record in designing and delivering large-scale data pipelines and integration architectures across the full data lifecycle
• Strong expertise in modern data platforms such as Databricks, Palantir Foundry, including batch, real time, and streaming data architectures
• Proven ability to ensure high data quality and reliability across pipelines, including influencing upstream systems and processes
• Strong experience in optimizing Spark workloads through effective partitioning, caching, and query performance tuning
• Solid understanding of data governance, lineage, and data management frameworks in an enterprise environment
• Experience working in agile delivery models with a focus on prioritization, scalability, and continuous value delivery
• Strong communication and stakeholder management skills with the ability to bridge business and technology
• Experience applying AI driven automation or strong ability to drive the adoption of agentic AI to transform data engineering practices
• Experience in insurance or reinsurance business processes is a strong advantage
• Strong analytical and problem-solving skills with a focus on root cause analysis
• Excellent organizational skills and a structured, objective driven mindset
• Strong commitment to quality and client orientation
• Proficiency in English; French is a plus
• Willingness to travel

Required Education

Degree in a technical discipline (software engineering, engineering, mathematics, physics) or management with relevant technical expertise.