at J.P. Morgan
Bulge Bracket Investment BanksPosted a month ago
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**Lead Software Engineer - Java** shapes JPMorganChase's data strategy. Drive solution development for Corporate Data Catalog, Lineage, Entitlements, and Data Quality frameworks. Key duties include creative problem-solving, code development, AI-assisted engineering practices adoption, and enterprise toolchain utilization. Seek 5+ years of Java experience, strong data engineering foundations, and proven Agile leadership. Leverage skills in Java, Spring Boot, RESTful APIs, microservices, SQL/NoSQL databases, Kafka, Elasticsearch, Databricks, Snowflake, and modern data mesh patterns. Master CI/CD, JUnit, Mockito, Jenkins, and Spinnaker for application resiliency and security. Preferred: AWS, Docker, Kubernetes, React JS, TypeScript, AI tooling, and regulated data governance.
- Compensation
- Not specified
- City
- Houston
- Country
- United States
Currency: Not specified
Full Job Description
Location: Houston, TX, United States
As a Lead Software Engineer at JPMorganChase within the Corporate Data and Analytics Services team, you'll play a crucial role in advancing our data strategy and solutions. As part of the Data Core Engineering group within CDAS, you'll contribute to executing the CT Data Strategy by developing key data solutions, including the Corporate Data Catalog, Data Lineage, Entitlements, and Data Quality frameworks.
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
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Strong experience building enterprise Java services Java, Spring Boot, RESTful APIs and microservices at scale.
- Strong data engineering foundations SQL and NoSQL (Oracle, MongoDB), Kafka messaging, Elasticsearch or Gemfire for search and caching.
- Databricks or Snowflake, and modern data mesh / data product patterns.
- Ownership of test automation and CI/CD (JUnit, Mockito, Jenkins, Spinnaker), with accountability for application resiliency and security.
- Proven technical leadership in Agile teams design ownership, code quality standards, and mentoring engineers.
- 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
Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
- AWS and containerization (Docker, Kubernetes).
- Front-end development for platform UIs (React JS, TypeScript).
- Delivery using approved AI tooling (GitHub Copilot, Claude).
- Hands-on delivery of data catalog, metadata, lineage or data governance capabilities on a regulated platform
