Senior Lead Software Engineer - Front Office Trading

JPMorgan Chase & Co.•Jersey City, NJ

2d•$171,000 - $260,000

About The Position

As a Senior Lead Software Engineer at JPMorgan Chase within the Corporate Technology - Treasury & Chief Investment Office Technology team, you will be instrumental in building and supporting a mission-critical trade execution platform. Your work will directly impact front office outcomes, and you will collaborate closely with Front Office and Middle Office users to shape the technology roadmap across Python/Java, Kafka, AWS + Databricks, and SQL/NoSQL. We are looking for individuals passionate about solving problems through innovation and engineering practices. You will apply your expertise across the software development lifecycle and partner with stakeholders to achieve common goals in a collaborative, trusting, and thought-provoking environment that encourages diversity of thought and creative solutions.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience
Strong hands-on practical experience delivering system design, application development, testing, and operational stability
Advanced in one or more programming language and proficient in coding in one or more languages and frameworks such as Python, Pandas, Django or Java, Spring, MQ/Kafka
Experience with AWS & Databricks ecosystem technology stacks as EMR, redshift, Dynamo DB, Athena, S3, Unity Catalog, Delta Lake, Spark etc.
Experience with SQL and No-Sql Databases such as Oracle, MS Sql, PostgreSQL, Graph DB, Dynamo DB etc.
Proficiency in automation and continuous delivery methods
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.)
Practical cloud native experience and hands on experience in Cloud platforms like Kubernetes / Cloud Foundry
Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls.

Nice To Haves

Strong stakeholder management and the ability to align technical solutions with business goals.
Familiarity with investment banking products - fixed income, derivatives
Prior experience supporting trade execution platforms
Hands-on experience with GenAI Developer Tooling

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, and reviews and debugs code written by others.
Manages responsibility for supporting the day-to-day operations of the trade execution platform, collaborating with Middle Office and Front Office business users.
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.
Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies.
Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.