EU remote
Transversal TechLead — Coface Business Information
About this role
This is a hands-on technical leadership role. You are expected to remain close to implementation teams, review designs and code, mentor engineers, and actively support complex technical delivery challenges. You will collaborate closely with product teams, developers, and stakeholders to design scalable, secure, and data-led solutions that support Coface's strategic business growth and digital transformation objectives.
Working alongside Enterprise and Data Architects, you will bridge the gap between business requirements and technology execution — ensuring architecture/technical decisions are transparent, cost-effective, compliant, and future-proof. Key Responsibilities Functional responsibilities Collaborate with product teams to gather, clarify, and formalize business requirements. Work as a team with Enterprise and Data Architects to translate business problems into architectural solutions.
Analyze legacy systems and source code to understand current processes, data flows, and integration points. Document functional needs, architecture decisions, and Architecture Decision Records (ADRs) in Confluence. Conduct gap analyses between legacy systems and new business needs, identifying technical debt. Design modular, scalable functional architectures and phased transition strategies. Define integration points and coexistence patterns for parallel operation with legacy systems.
Maintain a technical debt register and propose remediation roadmaps aligned with business priorities. Technical responsibilities Select appropriate technology stacks through Proof of Concepts (POCs) and benchmarks. Design and implement robust, scalable technical architectures. Ensure alignment between functional requirements and technical designs. Conduct code reviews to enforce architectural standards and best practices.
Define and enforce non-functional requirements (NFRs): performance, availability, resilience, scalability, and capacity planning. Design for observability: structured logging, distributed tracing, monitoring, and alerting. Evaluate and recommend integration patterns: REST APIs, event-driven architecture (Kafka, RabbitMQ), ETL/ELT pipelines, iPaaS, and ESB where appropriate. Contribute to AI/ML platform architecture decisions, including model serving, data pipelines, and Agentic AI workloads.