Lead Software Engineer - Java, Cloud & Streaming Technologies

Lead Software Engineer - Java, Streaming, AWS

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorganChase within the Consumer and Community Banking Credit Card Pricing, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within Credit Card Pricing Business domain in support of the firm’s business objectives.

Job responsibilities

• Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems.
• Develops secure high-quality production code, reviews and debugs code written by others.
• Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems.
• Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies.
• Adds to team culture of diversity, opportunity, inclusion, and respect.
• Lead design and delivery of cloud-native services using Java 11/17/21, Spring Boot, PostgreSQL, Kafka and AWS services. Produce secure, high‑quality, production-grade code and review PRs for peers.
• Apply design patterns and software design principles (SOLID, DRY, 12‑Factor, hexagonal/clean architecture) to create maintainable, scalable solutions and articulate trade‑offs.
• Build and optimize event-driven architectures with Apache Kafka (topic design, producers/consumers, schema evolution, delivery semantics, performance tuning).
• Own service lifecycle and operational stability: CI/CD pipelines, automated testing, observability (logging/metrics/tracing), incident response, reliability and resiliency patterns.
• Partner with product and architecture to align roadmaps, APIs, and standards; proactively communicate status, risks, and technical decisions to stakeholders.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years of applied experience, including experience as a Technical Lead
• Hands-on practical experience delivering system design, application development, testing, and operational stability.
• Advanced in one or more programming language(s). Must have deep technical knowledge on Java. Also good working knowledge on Python.  
• Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security.
• Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
• In-depth knowledge of the financial services industry and their IT systems.
• Practical cloud native experience.
• Strong hands‑on expertise with Java 11/17/21 and Spring Boot; proficiency with build tooling (Maven/Gradle) and dependency management.
• Advanced SQL and relational data modeling with PostgreSQL; performance tuning, indexing, query plans, and schema migration tooling (Flyway/Liquibase).
• Production experience with Apache Kafka (consumer groups, partitions, idempotence, exactly‑once/at‑least‑once strategies, monitoring).

• Practical experience building on AWS (e.g., ECS/EKS/EMR, Lambda, API Gateway/ALB, S3, IAM, CloudWatch, Parameter Store/Secrets Manager); familiarity with IaC (Terraform/CloudFormation).

  
  
  
  Preferred Qualifications 

• Familiarity with how Large Language Models (LLMs) work at a high level, including concepts like using “agents” to break tasks into steps, basic planning and reasoning approaches, and related techniques.
• General awareness of major LLMs and what they’re good at (e.g., strengths, limits, typical use cases), without needing deep specialization.
• Experience deploying basic ML or AI-powered services on the cloud, preferably AWS.
• Python skills for building data-driven features and simple AI pipelines.