Lead Software Engineer - Investment Portfolio Technology

JPMorgan Chase & Co.•Jersey City, NJ

8d•$156,750 - $215,000

About The Position

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 Asset Management (AM) Technology organization, 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. This is an exciting opportunity to join a passionate team dedicated to building products that truly help our users. As a Lead Software Engineer within AM Technology, you will be a hands-on engineer responsible for designing and delivering secure, resilient, and scalable platforms that power Customized Managed Account Solutions (CMAS) investing and Separately Managed Accounts (SMA) at retail scale. You will lead key components end-to-end (design, delivery, production stewardship) and partner closely with Product, Business, and Operations stakeholders. You will lead through technical direction, deep delivery ownership, and raising engineering standards across teams.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience
Advanced knowledge of application, data, and infrastructure architecture
Hands-on practical experience delivering platform/system design, application development, performance testing, incident management and operational stability
Advanced in one or more programming language(s) – Java and Python
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 practice
Proficient in all aspects of the Software Development Life Cycle and advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
Experience building RESTful web services, microservices, API design, testing, performance tuning, and secure coding.
Strong AWS experience with production-grade services and patterns.
Strong experience designing and operating data solutions in Snowflake (schema design, performance, secure access, operationalization).
Strong SQL skills; experience with stored procedures and relational concepts.

Nice To Haves

AWS certification(s) – Developer, Solutions Architect, ML Engineer, AI Developer, DevOps Engineer, CloudOps Engineer
Airflow (or equivalent orchestration) experience
Modern CI tooling experience – GitHub, BitBucket, GitLab, Jenkins, etc.
Financial Services experience and familiarity with trading concepts/trade lifecycle.

Responsibilities

Design, build, test, and deploy Java-based microservices and REST APIs (with Python where appropriate) supporting SMA trading, data workflows, and client servicing; lead delivery across multiple services/teams by driving execution, managing dependencies and trade-offs, enforcing alignment to established architecture/engineering guardrails, and continuously improving standards and practices through code reviews and adoption of proven industry best practices.
Build and evolve data pipelines and curated datasets used for trading, client reporting, operational insights, and controls; partner with data governance, risk, and controls stakeholders to ensure lineage, quality, access controls, and auditability; drive Snowflake best practices (performance, cost controls, secure data sharing patterns, environment hygiene).
Own production readiness (observability, resiliency, incident response, post-incident improvements); eliminate recurring issues through automation and platform fixes; establish SLOs/error budgets; support production as needed and ensure rapid triage/root-cause resolution.
Embed security, resiliency, and controls into design and implementation; ensure adherence to IT control policies and corporate standards; raise the bar on code reviews, testing strategy, release governance, and documentation quality.
Partner continuously with stakeholders to translate business outcomes into technical delivery plans; participate in agile planning and provide engineering leadership on scope, sequencing, and delivery risk.
Foster a culture of inclusion, continuous learning, experimentation, and high engineering standards through design reviews, operational leadership, and engineering best practices.
Model safe and effective use of enterprise-authorized AI-assisted development tools (coding, tests, troubleshooting, documentation), including validation expectations and secure handling of sensitive inputs/outputs.
Drive 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.