Senior Lead Software Engineer – Full Stack, Retail Solutions

Hace 2 días

Madrid, España Marsh Jornada completa
ResponsibilitiesLead the design, development and operation of new capabilities across our SaaS products.
Make architectural decisions that balance immediate business needs with long‑term scalability and maintainability.
Work closely with product, engineering and business leadership to shape product roadmaps and technical strategy.
Identify and drive improvements across performance, reliability, scalability, security and developer experience.
Champion modern engineering practices, including AI‑assisted development tools such as GitHub Copilot.
Lead the adoption of new patterns, frameworks and platform capabilities across teams.
Mentor engineers and foster a culture of continuous learning, operational excellence and high‑quality delivery.
Partner with platform, security and compliance teams to ensure our products meet organisational and regulatory standards.
Contribute directly to production code and remain deeply involved in technical implementation.
Requirements7+ years of software engineering experienceExperience as a Senior Engineer, Lead Engineer, Staff Engineer or Software ArchitectA degree in Computer Science, Engineering, Data Science or a related field (preferred but not essential)Certifications such as Azure Solutions Architect or CKA (beneficial but not required)Experience working in consulting or client‑facing environments (helpful but not essential)Building and operating modern full‑stack applications using technologies such as JavaScript/TypeScript and PythonFrameworks such as Express.
Js, Vue.
Js, Flask and PandasDesigning distributed systems, service‑oriented architectures and microservicesCloud‑native development, ideally on Azure Kubernetes‑based platforms and deployment practices AKS and supporting technologies such as ArgoCD, Helm, Dapr and GitOps workflowsDatabase technologies including MongoDB and PostgreSQLDevSecOps, Infrastructure as Code and CI/CD practicesLarge‑scale data platforms, data warehouses or lakehouses (Databricks experience is advantageous)