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Overview In this role, you own end-to-end data engineering across SCOR’s governed data platform, building scalable pipelines (batch, real-time, streaming) to serve analytics and AI. You’ll lead cross-functional teams across multiple domains to ensure high-quality, interoperable data delivery and drive data quality at the source. You’ll advance data engineering practices with agentic AI to accelerate pipeline development and reliability, aligning business logic with technology. This position shapes analytics product delivery in a global reinsurer context with a strong focus on governance and impact.

Verantwortungsbereiche Design and implement scalable data integration pipelines (batch, real-time, streaming) for analytics and AI use cases Enforce engineering best practices to ensure reliable, high-performance pipelines Bridge business logic, technical semantics, and system interoperability for governed data delivery Provide clean, contextualized data to analytics products, dashboards, and AI initiatives Align data models, semantic layers, and data product definitions across domains Collaborate with business and IT to identify data quality issues at the source and embed quality into evolution Translate requirements into scalable integration designs Define and enforce standards for data engineering and integration Ensure pipeline performance, latency, data freshness, and reliability through monitoring Lead cross-functional teams in agile delivery, prioritizing by business impact Drive

data engineering transformation with agentic AI for AI-assisted pipeline development and testing Zentrale Anforderungen Extensive hands-on experience in data engineering 4+ years in a leadership role with ability to lead engineering teams or large cross-functional initiatives Entrepreneurial mindset with ownership and accountability for end-to-end data solutions Proven track record delivering large-scale data pipelines and integration architectures Expertise in modern data platforms (Databricks, Palantir Foundry) including batch and streaming Strong data quality and reliability influence across pipelines and upstream systems Experience optimizing Spark workloads (partitioning, caching, tuning) Solid data governance, lineage, and management understanding in enterprise environments Experience in agile delivery with prioritization, scalability, and value delivery Strong communication and

stakeholder management to bridge business and technology Experience applying AI-driven automation or agentic AI in data engineering Insurance or reinsurance domain experience is a plus Strong analytical and problem-solving skills with root cause focus Excellent organizational skills and objective-driven mindset English proficiency; French a plus Willingness to travel strong communication stakeholder management leadership and team development Databricks Palantir Foundry batch, real-time, streaming architectures