Lead Software Engineer – Portfolio Insights Platform

JPMorganChase

Greater London

Hybrid

GBP 110,000 - 150,000

Full time

6 days ago

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Job summary

J.P. Morgan is seeking a Lead Software Engineer for Connect Wealth's Portfolio Insights Construction team in London to build platforms serving position and account data for Wealth Management advisors.

You will deliver real-time alerting, analytics, and client dashboards while guiding engineering excellence and platform reliability. You will collaborate across teams, design scalable microservices, and drive architectural decisions with a focus on secure, production-grade code and AI-assisted

Qualifications

Formal training or certification in software engineering concepts.
Advanced hands-on experience with Java and Spring Boot in a microservices architecture.
Practical experience with AWS cloud-native patterns: Lambda, Step Functions, Terraform-managed infrastructure, and relational databases at scale.
Working knowledge of event-driven systems, including Kafka producers/consumers and consumer groups.
Demonstrated experience leading effective use of approved AI-assisted development tools, with strong validation skills for AI output.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity and secure handling.
Proficient across the full SDLC, with advanced understanding of agile delivery, CI/CD, application resiliency, and security practices.
In-depth knowledge of financial services IT systems, ideally wealth/advisory data.

Responsibilities

Execute creative software solutions across ingestion, resolution, and serving layers of the platform.
Design, develop, and troubleshoot technical challenges, breaking down complex data-reconciliation problems.
Develop secure, high-quality production code in Java/Spring Boot and Python/Java Lambdas.
Review and debug code, including cross-service and cross-repo changes.
Drive adoption of enterprise-authorized AI-assisted engineering practices and establish validation standards for AI-generated code.
Apply SDLC toolchain and AI-assisted development capabilities to improve delivery velocity.
Identify and automate remediation of recurring operational issues to enhance platform stability.
Lead evaluation of architectural approaches for data-platform challenges and drive technical discussions with internal teams.