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As a Lead Software Engineer at JPMorgan Chase within the Credit Data Pond team housed within Wholesale credit risk technology, 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
As a Lead Software Engineer at JPMorgan Chase within the Credit Data Pond team housed within Wholesale credit risk technology, 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
- 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.
- Establishes clear “where AI helps / where it must not” guardrails for the SDLC (design, coding, testing, deployment, operations), ensuring sensitive data handling and secure engineering practices are consistently applied.
- Implements a repeatable AI-assisted review workflow (PR checklist, quality gates, and approval criteria) that improves readability, reduces defects, and enforces standards for maintainability, performance, and security.
- 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.
- Selects and standardizes the right tools across planning, code, build, test, security scanning, release, and operations—reducing fragmentation and improving end-to-end traceability.
- Designs CI/CD pipelines with measurable outcomes (lead time, deployment frequency, change failure rate), using automation to remove manual steps and improve release reliability.
- 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
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and advanced applied experience Python
Hands-on practical experience delivering system design, application development, testing, and operational stability - Advanced in one or more programming language(s)
- 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.
- Defines a practical operating model for AI-assisted development (approved use cases, do/don’t guidance, and escalation paths) so teams use tools consistently and responsibly.
- Establishes validation standards for AI outputs—mandatory peer review, secure coding checks, and automated test requirements—before any AI-assisted change is merged or released.
- 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
- Proficient in all aspects of the Software Development Life Cycle
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
- In-depth knowledge of the financial services industry and their IT systems
- Practical cloud native experience
Preferred qualifications, capabilities, and skills
- Advanced Apache Spark experience (PySpark/Spark SQL), including performance tuning (partitioning, shuffle optimization, caching), troubleshooting, and designing scalable batch/stream processing patterns.
- Deep lakehouse and data platform expertise (e.g., Delta Lake concepts, schema evolution, data quality frameworks, lineage/metadata, and governance patterns) applied in production environments.
- Strong DevSecOps and platform engineering capability, including CI/CD design, automated security/quality gates, observability (logs/metrics/traces), SLOs/SLAs, and incident management/RCA in regulated environments.
- Proven end-to-end delivery leadership for large, cross-functional initiatives (roadmaps, dependency management, stakeholder alignment, budgeting/capacity planning, and measurable outcomes tied to reliability, cost, and time-to-market).
- Experience driving responsible, enterprise-approved AI-assisted engineering adoption, including coaching teams on safe use, validation standards for AI outputs, and building reusable “golden path” templates/pattern libraries (Terraform modules, Databricks job templates, pipeline scaffolds).
