Backend Java Software Developer

Overview

Backend Java Software Developer on a generative-AI enabled squad delivering a microservices transformation of the Financial Risk Analytics (FRA) platform at S&P Global Market Intelligence.

What you'll do

• Design and deliver high-quality domain microservices, REST/GraphQL APIs, and event-driven integrations that are scalable, decoupled, and future-ready.
• Engineer robust asynchronous workflows and event consumers/producers for resilient services in complex distributed environments.
• Own the design and optimisation of service-owned data stores, supporting performance, scalability, and data migration from legacy monolithic platforms.
• Drive engineering excellence through unit testing, integration testing, performance tuning, and active code review.
• Monitor live services, resolve incidents, and continuously improve observability to support secure, stable, high-performing production systems.

What you'll need

• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related discipline; equivalent professional experience may also be considered.
• 5 to 9 years of work experience developing backend applications and microservices using Java and Spring Boot.
• Generative AI development and integration (microservices and APIs).
• Scala, Java / Spring Boot and/or .NET and/or Python.
• SQL / NoSQL / Cassandra / HBase / Spark / Yarn.
• Docker.
• Git.
• Collaborative problem-solving in a microservices environment across engineering, product, platform, and architecture teams.
• Ownership and accountability for service quality end to end, from build and test through production support.
• Adaptability and learning agility in fast-moving modernisation programs, including integration with legacy and near-real-time systems.
• Attention to quality and operational discipline, with care for testing, performance, observability, and production resilience.

Nice to have

• Kubernetes.
• Big data technology (Spark, Hadoop etc).
• Event streaming.
• Cloud-native development.
• Systems thinking across services, containers, infrastructure, queues, events, and data flows.
• Clear technical communication with platform teams, service owners, and product stakeholders.
• Adaptability and continuous learning across Kubernetes, cloud-native practices, and distributed integration patterns.
• Operational composure and resilience during incidents involving delayed messages, replay issues, scaling problems, and intermittent production faults.

Details

• Location: Noida, Uttar Pradesh, India.