Interview process: First step: Codility test, followed by 4 stages of interviews
No C2C or 1099
Job Summary:
The Operational Tooling team builds and evolves the strategic user-facing platform that supports the creation, governance, monitoring and operational management of financial indices across the FTSE Russell ecosystem. The role is focused on workflow-led, data-heavy interfaces, auditability, approvals, exception handling, dashboarding, and integrations across services.
You will work on applications that help operational users configure financial index artefacts. The role sits at the intersection of UX, workflow, data and platform engineering, with a strong emphasis on building software that is reliable, explainable, scalable and fit for regulated financial operations.
React is the primary technology for new front-end development. Angular remains an important secondary capability because parts of the existing platform and supporting modules are Angular-based and need to be maintained, extended or progressively modernised. On the backend, the role requires strong Java and Spring Boot engineering, API integration, cloud-native delivery patterns, and secure handling of operational and audit-sensitive data.
This is also an AI-first engineering role. The successful candidate should be well versed in AI-assisted and agentic development processes and should approach delivery with an AI-first mindset: using AI responsibly to accelerate prototyping, implementation, testing, documentation, code review preparation and design exploration, while applying strong human judgement, security awareness, validation discipline and full accountability for production outcomes.
Responsibilities
Design, develop and deliver full-stack solutions that support operational index workflows, governed change, monitoring and investigation use cases.
Build modern, maintainable front ends using React for new UI capabilities.
Support and extend Angular-based modules where needed, including shared platform components
Engineer robust backend services using Java and Spring Boot, with well-structured APIs, clear domain boundaries and strong operational supportability.
Work closely with Product Owners, Business Analysts, UX, QA, platform engineers and architects to shape solutions from problem statement to production release.
Contribute to modular UI, component library and micro-frontend style approaches where they improve reuse, delivery speed and maintainability.
Implement integrations across workflow, audit, notification, analytics and platform services in a way that preserves traceability and operational control.
Use AI-assisted and agentic development approaches to accelerate delivery, including prototyping, design exploration, code generation, refactoring, test creation and documentation support.
Validate AI-generated outputs rigorously, ensuring that changes are secure, correct, supportable and suitable for enterprise financial systems.
Maintain high engineering standards through code reviews, automated testing, CI/CD discipline, observability and proactive operational ownership.
Help drive clarity where requirements are incomplete by breaking down ambiguity, identifying risks early and proposing pragmatic delivery options.
Contribute to a modern engineering culture that values accountability, reuse, maintainability, measurable outcomes and continuous improvement.
Requirements
Strong professional experience delivering full-stack applications in complex enterprise environments.
Deep hands-on experience with React and Angular, including component design, state handling, data-driven UIs, accessibility and performance-conscious front-end development.
Strong Java experience with solid Spring Boot / Spring Framework delivery in production systems.
Experience building and integrating REST and/or GraphQL APIs.
Experience with relational and/or NoSQL data stores, including designing solutions for operational, audit or workflow-heavy use cases.
Experience working with cloud-hosted systems and modern application delivery patterns, ideally in AWS-style environments.
Experience with event, queue or message-based integrations such as Kafka, MQ, SQS/SNS or equivalent patterns.
Strong understanding of automated testing across unit, integration and UI/component layers.
Experience with CI/CD pipelines, source control and modern software delivery practices.
Strong verbal and written communication skills, including the ability to explain technical trade-offs to both engineers and non-engineers.
Evidence of delivering effectively through ambiguity and taking ownership for outcomes, not just assigned tasks.
Practical experience using AI-assisted engineering tools in day-to-day delivery.
Clear understanding of agentic development principles, including task decomposition, prompt discipline, validation, guardrails, secure handling of context and human accountability for outputs.