Principal Software Engineer - Java, AWS, RESTful

If you are looking for a game-changing career, working for one of the world's leading financial institutions, you’ve come to the right place.

As a Principal Software Engineering at JPMorgan Chase within the Chief Data and Analytics Organization, you provide expertise and engineering excellence as an integral part of an agile team to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way.. Your expertise is applied cross-functionally to evolve firm wide Data Mesh, AI/ML and GenAI, and Data Governance platform. You will play a pivotal role in driving advanced technical capabilities/frameworks, and collaborate with colleagues across the organization to deliver scalable, high-performance, and best-in-class platform. 

Job responsibilities

• Defines the technical vision and architectural direction for back-end services
• Provides technical leadership through hands-on contribution and mentoring: contribute high-quality code, perform code reviews, and champion best practices in software engineering, including design patterns, testing methodologies, and operational excellence
• Leads critical design decisions: data models, consistency trade-offs, API contracts, failure modes, with full ownership from design through production. Resolve unforeseen engineering obstacles effectively
• Defines and maintain libraries, SDKs, and frameworks that become the default building blocks for engineering teams across the organisation, reducing duplication and accelerating delivery
• Drives reliability, performance, and cost efficiency across services. Embed security, compliance, and data-privacy considerations into architecture decisions from the outset
• Shapes the technology roadmap by evaluating emerging tools and established techniques, balancing cost, complexity, and performance
• Harnesses AI and approved coding-assist tools as core enablers of day-to-day work, delivering measurable gains in code quality, velocity, and team productivity. Continuously deepen technical. and domain expertise to evaluate and adopt emerging technologies that strengthen scalability, resilience, and security across Global Technology
• Partners with Product and Engineering leadership to align technical strategy with business objectives
• Architects and governs agentic AI-enabled engineering workflows (using enterprise-authorised tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale (e.g., AI-driven PR review assistance, test generation/maintenance, release readiness checks, incident triage and root-cause acceleration), while defining guardrails for validation, security, resiliency, and reuse across teams
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorised AI-assisted development and automation capabilities, to improve the value realised by automation at scale

Required qualifications, capabilities, and skills

• 12+ years of engineering leadership, while having an overall of around 20 years experience and 5+ years at staff/principal level, with ability to manage multiple complex assignments, driving engineering deliveries and work with geographically dispersed cross-functional teams
• Expert-level proficiency in Java 17+. Expert knowledge of OOP/OOD, performance optimisation, concurrency and parallelism with understanding of other programming paradigms like functional, event-driven, reactive, and metaprogramming
• Expert-level understanding of RESTful architecture: resource modelling, idempotency, caching, pagination, rate limiting, version strategies, contract-first development with OpenAPI, track record of defining API standards adopted across engineering organisations
• Working knowledge of modern front-end architectures and frameworks, sufficient to review UI integration approaches, advise on API consumption patterns, and ensure alignment between front-end and back-end teams on contracts, error handling, and performance expectations
• Expertise across multiple architectural paradigms: event-driven, CQRS, SOA, hexagonal, serverless, domain-driven design, saga patterns, and pipe-and-filter, with the judgement to know which to apply, when to go hybrid, and when not to
• Proven track record in designing and delivering large-scale distributed systems and microservices architectures with high availability and fault tolerance plus fluency in consensus, partitioning, replication, and consistency models, with the judgement to define service boundaries, manage inter-service communication, and navigate implied operational complexity at scale
• Expert-level proficiency with AWS and cloud-native architecture. Specifically EKS, and AWS Networking. Broad working knowledge of the wider AWS ecosystem: compute, serverless, messaging, storage, and IAM, with the architectural judgement to select, combine, and optimise services for cost, resilience, and performance at scale
• Substantive experience with databases at scale such as Neo4j, PostgreSQL, and with streaming and messaging platforms such as Kafka, Kinesis, or Flink with extensive expertise in observability across distributed systems: metrics, logging, tracing, and alerting, with practical knowledge of Datadog and AWS CloudWatch plus ability to define instrumentation standards, establish SLOs/SLIs, and build observability practices
• Strong understanding of QA and test automation strategies: unit, integration, contract, and end-to-end. Able to define testing standards, champion shift-left practices, and guide teams in building reliable tests suites that scale with distributed systems
• Demonstrated experience designing and leading adoption of agentic AI-enabled development practices (using enterprise-authorised tools within the work environment) across teams, including setting standards for human-in-the-loop validation, auditability/traceability of changes, and secure handling of sensitive data

• Strong understanding of responsible AI use and control expectations in engineering workflows, including security/resiliency implications, data sensitivity, and risk-based governance; ability to influence senior technical leaders on safe scaling patterns and reuse