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Deutsche Bank

New

Lead Full Stack Engineer (Dealer-to-Client eTrading)

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Summary

Lead Full Stack Engineer at Deutsche Bank modernizing a large-scale dealer-to-client eTrading platform, migrating backend services to the cloud, and building React-based web interfaces. Ensures scalability, compliance, and performance while mentoring engineers and collaborating across stakeholders.

  • Take a leading role in the modernization and cloud migration of one of the world’s largest trading platforms
  • Enforce architectural principles, coding standards, and best practices across backend and frontend teams
  • Migrate backend services to the cloud ensuring scalability, resilience, and security
  • Drive the design and development of modern React-based web interfaces and their integration with backend trading services
  • Ensure compliance with international regulations and support internal and external audits, security assessments, and performance tests
  • Collaborate with senior stakeholders, architects, and engineers across business and technology teams
  • Mentor and guide other engineers, helping to build a high-performing development culture
  • You love this job but feel you cannot tick 100% of the boxes? Send us your CV anyway!
  • Solid knowledge of modern software architecture (microservices, event-driven systems, APIs, messaging middleware)
  • Strong experience in cloud technologies (AWS, GCP, or Azure) and containerization/orchestration (Docker, Kubernetes)
  • Deep expertise in either Java or React, with strong hands-on experience in both
  • Advanced knowledge of backend engineering (Java concurrency, multithreading, networking, JVM tuning, performance profiling)
  • Proven track record of designing and developing distributed, high-performance, and scalable systems
  • Ability to review, challenge, and improve designs and codebases of other engineers
  • Strong understanding of frontend engineering at scale (React, TypeScript, testing frameworks, performance optimization)
  • Experience in CI/CD pipelines, DevOps practices, observability (logging, metrics, tracing)
  • Proven ability to leverage AI tools to enhance productivity, optimise workflows to solve business problems, while applying critical judgment to ensure responsible and ethical use of data and AI outputs

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