Full Stack Software Engineer III

About the role

This role is for an experienced Software Engineer III who will own end-to-end delivery of secure, full stack features that power critical Global Banking businesses. You will shape solution options, drive design decisions, and implement scalable software that addresses ambiguous, multi-dimensional problems in a high-stakes environment. You will raise the engineering bar by authoring secure production code, leading rigorous code reviews, and debugging complex cross-service issues while continuously improving standards and hygiene. The role provides opportunities to deepen technical influence, explore AI and GenAI patterns responsibly, and build a long-term career within a firm that invests deeply in its people and operational excellence. You will troubleshoot intricate production incidents, drive performance and resiliency improvements, and automate recurring problems to strengthen operational stability at scale. This is a role for someone who takes ownership, embraces fast feedback, and thrives in a culture of strong architecture, rigorous reviews, and scalable delivery. You will partner closely with product, security, and operations to turn requirements into solid designs and shipping clean, maintainable Java services.

Key facts

What you'll do

• Lead delivery of complex features and services by shaping solution options, driving design decisions, and implementing scalable software that addresses ambiguous, multi-dimensional problems across global banking workflows.
• Own end-to-end engineering outcomes across design, build, test, release, and operate phases - ensuring security, resiliency, performance, and maintainability in production for customer-critical systems.
• Set a high bar for code quality by authoring secure production code, leading code reviews, and debugging complex cross-service issues while continuously improving engineering standards, practices, and hygiene.
• Drive operational excellence by identifying systemic failure patterns, implementing automation such as self-healing mechanisms, alerting, and runbooks, and delivering sustainable fixes that measurably improve stability and resiliency.
• Influence architecture and platform alignment by contributing to service boundaries, API and event contracts, data flows, and non-functional requirements across multiple teams while working within enterprise constraints and standards.
• Partner on technical evaluations with internal stakeholders, producing outcomes-oriented assessments of architecture, delivery readiness, security posture, and fit with existing platforms and information architecture to guide investment decisions.
• Mentor and elevate team capability through pairing, design reviews, knowledge sharing, and contributing to communities of practice that accelerate adoption of modern engineering practices and patterns.
• Promote an inclusive engineering culture by supporting a team environment grounded in diversity, opportunity, inclusion, and respect in all collaboration and decision-making processes.
• Produce architecture and design artifacts for complex applications, ensuring that design constraints are rigorously met throughout the full software code development lifecycle from conception to deployment.
• Leverage enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity, using them for code generation, refactoring, and unit test creation while validating outputs through peer review, automated testing, and secure coding standards.
• Apply knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation and reduce manual overhead.
• Troubleshoot complex production issues by correlating logs, metrics, and traces across distributed services, driving rapid resolution and implementing preventative measures to avoid recurrence.
• Collaborate with cross-functional partners to define and prioritize technical enablers, platform improvements, and infrastructure upgrades that support scalable and reliable banking solutions.
• Optimize event-driven architectures and Kafka-based workflows, focusing on reliability patterns such as idempotency, retries, ordering, and dead-letter queues to ensure robust message processing.
• Contribute to continuous improvement of CI/CD pipelines, build and test automation, and quality gates, enabling faster, safer delivery of features with high confidence in production readiness.

Requirements

• Formal training or certification on software engineering concepts and proficient applied experience in building and operating production software systems over time.
• Demonstrated ownership of system design and delivery for production services, including defining testing strategy, executing releases, and providing operational support with clear accountability for outcomes.
• Strong proficiency in Java and common frameworks such as Spring Boot, Hibernate, JPA, and Spring Kafka, including performance tuning, debugging, and troubleshooting patterns in real-world scenarios.
• Working knowledge of microservices and Kafka/event-driven architectures, with hands-on experience implementing reliability patterns such as idempotency, retries, ordering, and dead-letter queues to ensure system robustness.
• Experience implementing automation and continuous delivery, including building and testing automation, managing CI/CD pipelines, and enforcing quality gates across the software delivery lifecycle.
• Proficiency across the full software development lifecycle - gathering requirements, designing solutions, implementing code, testing functionality, releasing changes, and supporting operations - with clear, traceable documentation for stakeholders.
• Strong understanding of Agile delivery practices, application resiliency techniques, secure engineering practices, and operational readiness processes to support 24x7 banking services.
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment, demonstrating the ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security in line with enterprise policies.
• Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs and outputs, and adherence to resiliency, security, and compliance expectations when integrating AI into engineering processes.

Practical notes

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