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Tools in this posting

Java
Python
AWS
Spark
Scala
Databricks

Source

- Hands-on practical experience delivering system design, application development, testing, and operational stability - Advanced in one or more programming languages including Java, Python, or Scala. - Good understanding of the AWS platform, including its core services, architecture best practices, and security features.

- Advanced in one or more programming languages including Java, Python, or Scala. - Good understanding of the AWS platform, including its core services, architecture best practices, and security features. - Extensive experience with big data technologies, including Apache Spark, for large-scale data processing and analytics.

- Good understanding of the AWS platform, including its core services, architecture best practices, and security features. - Extensive experience with big data technologies, including Apache Spark, for large-scale data processing and analytics. - 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.

Preferred qualifications, capabilities, and skills - Exposure to Databricks - Experience with data visualization and business intelligence platforms.

Job description

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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 Corporate Technology in Financial Planning & Analysis team, 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

Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
Develops secure and high-quality production code, and reviews and debugs code written by others
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.
Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture

Required qualifications, capabilities, and skills

Formal training or certification on software engineering concepts and 5+ years applied experience
Hands-on practical experience delivering system design, application development, testing, and operational stability
Advanced in one or more programming languages including Java, Python, or Scala.
Good understanding of the AWS platform, including its core services, architecture best practices, and security features.
Extensive experience with big data technologies, including Apache Spark, for large-scale data processing and analytics.
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
Proficient in all aspects of the Software Development Life Cycle
Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
In-depth knowledge of the financial services industry and their IT systems
Practical cloud native experience

Preferred qualifications, capabilities, and skills

Exposure to Databricks
Experience with data visualization and business intelligence platforms.
Bachelor's degree in Finance, Technology, Data Science, or a related field; advanced degree preferred