Head of Data Engineering

pData 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. /ppAs 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. /ppYour 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. /ppA 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. /ppYou 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. /pbr/pbKey duties and responsibilities /b /pliDesign and implement robust, scalable, and reusable data integration pipelines (batch, real-time, streaming) to serve enterprise analytics and AI use cases /liliDrive engineering excellence by enforcing best practices to ensure reliable, scalable, and high-performance data pipelines /liliBridge business logic, technical semantics, and system interoperability to enable consistent and governed data delivery /liliProvision clean, structured, and contextualized data to support analytics products, dashboards, and AI initiatives /liliEnsure alignment with analytical data models, semantic layers, and data product definitions across domains /liliCollaborate with business and IT teams to identify and resolve data quality issues at source and embed data quality requirements into system evolution /liliTranslate business and analytical requirements into scalable technical integration designs /liliDefine and enforce best practices, frameworks, and standards for data engineering and integration /liliEnsure high performance and scalability of pipelines through monitoring of latency, data freshness, and reliability /liliDefine and track KPIs and SLAs (e.g. uptime, delivery performance) to ensure operational excellence /liliLead and develop cross-functional engineering teams, driving agile delivery and clear prioritization aligned to business impact /liliDrive the transformation of data engineering by leveraging agentic AI, enabling AI-assisted pipeline development and testing and alike /libr/pbRequired experience competencies /b /pliExtensive hands-on experience in data engineering /lili4+ years in a leadership role, with proven ability to lead engineering teams or large cross functional initiatives and influence senior stakeholders /liliDemonstrates an entrepreneurial mindset with a strong sense of ownership, taking accountability for delivering high impact data engineering solutions end to end /liliStrong track record in designing and delivering large-scale data pipelines and integration architectures across the full data lifecycle /liliStrong expertise in modern data platforms such as Databricks, Palantir Foundry, including batch, real time, and streaming data architectures /liliProven ability to ensure high data quality and reliability across pipelines, including influencing upstream systems and processes /liliStrong experience in optimizing Spark workloads through effective partitioning, caching, and query performance tuning /liliSolid understanding of data governance, lineage, and data management frameworks in an enterprise environment /liliExperience working in agile delivery models with a focus on prioritization, scalability, and continuous value delivery /liliStrong communication and stakeholder management skills with the ability to bridge business and technology /liliExperience applying AI driven automation or strong ability to drive the adoption of agentic AI to transform data engineering practices /liliExperience in insurance or reinsurance business processes is a strong advantage /liliStrong analytical and problem-solving skills with a focus on root cause analysis /liliExcellent organizational skills and a structured, objective driven mindset /liliStrong commitment to quality and client orientation /liliProficiency in English; French is a plus /liliWillingness to travel /lipbRequired Education /b /ppDegree in a technical discipline (software engineering, engineering, mathematics, physics) or management with relevant technical expertise. /p