Lead Software Engineer

Job Description

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 Securities Services Technology 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

• Researching, designing, and developing complex components for Ab Initio applications with quality coding
• Coding Ab Initio graphs and components using ETL tools (Ab Initio GDE) and ensuring they are in line with user requirements
• Tuning Ab Initio process by maximizing the use of components and MFS file system to achieve a reduction of total process time.
• Storing, retrieving, and manipulating data by building queries for system analysis and requirements
• Implementing design decisions using design patterns and plans in Ab Initio and creating UNIX wrapper scripts to handle the complex transformation logic
• SQL & Database Expertise: Advanced SQL skills and experience with databases like DB2, Oracle, or PostgreSQL.
• Cloud Integration: Hands-on experience with AWS services (S3, EC2, Lambda) and data lake/pipeline architectures.
• Leadership and Documentation: Ability to work independently with minimal supervision, create technical design documentation, and mentor team members.
• 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.
  
  

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Deep understanding of system architecture: databases (relational/NoSQL), authentication/authorization, API design, cloud infrastructure (AWS/Azure), containerization, CI/CD pipelines
• Previous Ab Initio development experience, including deployment architecture work and hands-on debugging/troubleshooting of graphs.
• Proven experience working with large data volumes, with strong knowledge of data integration, batch and delta processing, and data capture.
• Experience in setting up code migration, CI/CD pipelines (Jenkins), and migrating environments between development, test, and production.
• Ab Initio Administration: Expert knowledge in managing Ab Initio EME (Enterprise Meta~Environment), technical repository maintenance, GDE/Server key renewals, and server software installation.
• Development and Technical Skills: Strong proficiency in developing Ab Initio graphs, parallelism techniques (SMP/MPP), and high-volume data processing. UNIX and Scripting: Advanced shell scripting (Bash, Korn shell) and UNIX/Linux operating system knowledge are essential.
• Experience in ETL/Ab Initio development and administration. Cloud, S3, AI knowledge
• Proficiency in modern languages and frameworks (we use Next.js, Javascript, Python, Java/Spring Boot, React, but care more about your ability to learn and deliver)
• Experience evaluating and integrating AI/LLM capabilities into applications
• 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