Lead Software Engineer, Java Microservices/API

JPMorgan Chase & Co.; Plano (TX); Presencial; USD 140 000 - 200 000; Tempo integral

Resumo da oferta

JPMorgan Chase & Co. is seeking a Lead Software Engineer in Plano, TX to guide design and delivery of secure, cloud-native backend services and RESTful APIs using Java and Spring Boot.

You will drive technical leadership across the full SDLC, promote AI-assisted development, and ensure scalable, resilient systems. Responsibilities include implementing cloud-native services on AWS with ECS and Docker, optimizing PostgreSQL data models, and leading code reviews, testing, and deployment practices

Qualificações

• Formal software engineering experience in enterprise-grade apps.

Responsabilidades

• Lead the design, development, and delivery of scalable, secure, cloud-native backend services and RESTful APIs using Java, Spring Boot, and microservice architecture.

Conhecimentos

Java

Spring Boot

RESTful APIs

AWS

Docker

PostgreSQL

CI/CD

AI-assisted tooling

Observability

Security

Ferramentas

ECS

Terraform

Kafka

Descrição da oferta de emprego

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 Corporate Sector – Corporate Technology, 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 various business functions in support of the firm’s business objectives.

Job Responsibilities

• Lead the design, development, and delivery of scalable, secure, cloud-native backend services and RESTful APIs usingJava, Spring Boot, and microservice architecture.

• Provide technical leadership across the full software development lifecycle, including architecture, solution design, coding standards, implementation, testing, deployment, observability, and production support.

• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.

• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

• Drive engineering excellence by producing high-quality production code, performing rigorous code reviews, improving test coverage, and ensuring adherence to secure coding and organizational engineering standards; Design and implement resilient distributed systems that meet performance, scalability, availability, and maintainability requirements.

• Develop and deploy cloud-native services on AWS, leveraging services such as ECS, containerization with Docker, and automated CI/CD delivery practices.

• Design, optimize, and maintain data models, schemas, and queries inPostgreSQL, ensuring data integrity, performance, and scalability across distributed services.

• Produce and review architecture, technical design, and implementation artifacts for complex applications, ensuring alignment with enterprise architecture, security, and operational standards; Identify recurring technical or operational issues and lead automation, remediation, and resiliency improvements to reduce toil and improve application stability.

• Drive team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, testing effectiveness, documentation, troubleshooting, and operational outcomes.

• Establish team standards for validating AI-generated outputs, including secure coding review, correctness checks, performance validation, automated testing, and responsible handling of data; Lead technical evaluation sessions with internal stakeholders, vendors, and external partners to assess architecture designs, integration approaches, technical feasibility, and alignment with existing systems.

• Analyze application telemetry, operational data, quality metrics, and delivery patterns to identify trends and drive continuous improvement in engineering practices and system reliability; Contribute to engineering communities of practice and support initiatives focused on modern software engineering, cloud-native development, automation, AI-assisted delivery, and emerging technologies.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience

• Strong hands-on experience designing, developing, and maintaining enterprise-grade applications usingJava and Spring Boot.

• Demonstrated experience building and supportingRESTful APIs, microservices, and distributed service-oriented systems; Practical cloud-native development experience, preferably withAWS, includingECS, containers, and cloud deployment patterns.

• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.

• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices

• Hands-on experience withDocker, containerized applications, and modern deployment practice; Strong understanding of CI/CD pipelines, automated testing frameworks, release automation, and DevOps engineering practices.

• Solid experience withPostgreSQL, including relational data modeling, query optimization, indexing strategies, and performance tuning; Advanced understanding of the software development lifecycle, agile methodologies, application resiliency, observability, and secure software delivery.

• Proven ability to lead technical design discussions, evaluate implementation options, and make sound engineering decisions for complex systems; Experience reviewing code, mentoring engineers, and promoting engineering best practices across a team.

• Demonstrated experience adopting or leading effective use of approved AI-assisted software development tools for coding, code review, test acceleration, documentation, refactoring, or troubleshooting.

• Strong understanding of responsible AI usage in engineering workflows, including validation of AI outputs, data sensitivity considerations, secure handling of inputs and outputs, and adherence to security and resiliency expectations.

• Strong analytical, debugging, and problem-solving skills with the ability to diagnose complex technical issues across applications, infrastructure, and data layers; Excellent communication and collaboration skills, with the ability to influence technical direction and partner effectively with cross-functional stakeholders.

Preferred qualifications, capabilities, and skills

• Experience with synchronous REST integration patterns for latency-sensitive credit/bureau calls, plus event-driven and asynchronous processing using Kafka, Amazon SQS, or similar — including dual-persistence patterns (e.g., Aurora PostgreSQL for transactional data and Snowflake for analytics and long-term audit).

• Familiarity with API gateway technologies, service mesh concepts, and IDAnywhere / ADFS OAuth2 authentication/authorization patterns, plus edge-proxy vendor integration patterns (ELB → ECS → Secrets Manager → edge proxy → vendor API) and API lifecycle management for third-party bureau integrations.

• Experience with infrastructure-as-code tools such as Terraform (Atlas / TFE), , or similar automation frameworks, and CI/CD delivery with blue/green deployments; Hands-on experience with observability tools, including logging, metrics, tracing, dashboards, and alerting platforms (Splunk, Dynatrace, CloudWatch, Grafana), and driving production readiness for regulated, high-availability services.

• Experience designing systems for high availability, fault tolerance, scalability, and operational resilience — including ECS NextGen cluster migrations, multi-AZ resiliency, and graceful degradation for downstream vendor outages; Familiarity with performance testing, load testing, and application tuning (BlazeMeter / JMeter) for high-throughput, low-latency distributed systems under real-time credit-decisioning SLAs.

• Experience with modern security practices, including secrets management (AWS Secrets Manager, Hera KMS, AppProtect), vulnerability remediation , dependency management, secure API design and controls for 15-year audit retention and consumer credit data (soft-pull vs hard-pull semantics); Experience leading technical modernization, platform migration, and cloud adoption— and application resiliency initiatives across a regulated environment; Track record of contributing to engineering communities of practice, reusable frameworks, and technical standards , and mentoring engineers towards consistent, secure, scaling delivery.