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.
Required qualifications, capabilities, and skills
- 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