Software Development/Engineering

The Team: Establish FRA Prototype Team & Delivery Approach

A full-scale microservices transformation of the S&P Globals Financial Risk Analytics product, pre-AI, was expected to require a large pool of technology roles across architecture, engineering, platform, security, operations, and delivery.

For the initial FRA prototype transformation, we want to stand up a lean, generative-AI enabled transformation squad with a focused team structure sized to deliver a validated microservices foundation.

The objective is to establish an agile technology transformation group to design and deliver a commercially aligned, targeted microservices architecture for the FRA platform, prioritizing measurable business outcomes and rapid learnings.

Responsibilities and Impact:

• Play a central role in building the organisation’s next-generation digital platform, developing microservices and backend components that power interactive and near-real-time customer experiences.
• Design and deliver high-quality domain microservices, REST/ GraphQL APIs, and event-driven integrations that enable scalable, decoupled, and future-ready systems.
• Engineer robust asynchronous workflows and event consumers/producers, helping create resilient services that perform reliably in complex distributed environments.
• Own the design and optimisation of service-owned data stores, supporting performance, scalability, and the successful migration of data from legacy monolithic platforms.
• Drive engineering excellence through unit testing, integration testing, performance tuning, and active code review, ensuring every release meets high standards of quality and maintainability.
• Contribute to operational confidence by monitoring live services, resolving incidents, and continuously improving observability to support secure, stable, and high-performing production systems.

What We’re Looking For:

Key qualifications for the job

• Generative AI Development & Integration (Microservices and APIs)
• Scala, Java / Spring Boot and/ or .NET and / or Python
• SQL / NoSQL/ Cassandra/ HBase/ Spark/ Yarn
• Docker
• Git

Key soft skills

• Collaborative problem-solving — Success in a microservices environment depends on working effectively across engineering, product, platform, and architecture teams. This role needs someone who can solve issues collectively, contribute constructively in design discussions, and build strong technical partnerships.
• Ownership and accountability — The strongest candidates take responsibility for service quality end to end, from build and test through to production support. They are proactive, dependable, and committed to resolving issues rather than handing them off.
• Adaptability and learning agility — Modernisation programs evolve quickly, especially when integrating with legacy platforms and near-real-time systems. This role requires someone who can absorb new domain knowledge fast, adapt to changing priorities, and remain effective in a fast-moving environment.
• Attention to quality and operational discipline — Building backend services is not just about writing code; it requires a mindset of precision, reliability, and continuous improvement. The individual should care deeply about testing, performance, observability, and building services that are resilient in production.

Additional key qualifications for the job

• Kubernetes
• Messaging platforms
• Event streaming
• Cloud-native development

Additional soft skills

• Systems thinking — In Kubernetes, messaging, and event-streaming environments, issues rarely sit in one component. Engineers need to understand how services, containers, infrastructure, queues, events, and data flows interact so they can anticipate downstream impact, design more resilient solutions, and troubleshoot effectively across the whole system rather than just their own code.
• Clear technical communication — Cloud-native and event-driven architectures create more service boundaries, contracts, and operational dependencies. Engineers must communicate clearly with platform teams, other service owners, and product stakeholders to avoid misunderstanding around APIs, event schemas, ownership, deployment changes, and incident response.
• Adaptability and continuous learning — Kubernetes, cloud-native practices, and distributed integration patterns evolve quickly, and real-world implementations often differ from ideal architecture. The role needs someone who can learn fast, absorb new patterns, and adjust to changing tooling, operating models, and migration realities without losing delivery momentum.
• Operational composure and resilience — Distributed systems fail in more complex ways: delayed messages, replay issues, scaling problems, noisy alerts, and intermittent production faults. Engineers need to stay calm under pressure, work methodically through ambiguity, and make sound decisions during incidents to protect service reliability and business continuity.