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Lead Software Engineer - Python, SQL
lead · Technology / Software Development
We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.
As a Lead Software Engineer at JPMorgan Chase within the Commercial & Investment Bank’s Credit Technology team, you will join an agile engineering team building real-time and end-of-day Risk & PnL platforms for Bonds, Loans, Credit Derivatives, Exotics products etc. You will design and deliver secure, resilient, low-latency, and scalable services that power front-office risk, trading, and management reporting workflows. You will be part of technical delivery across multiple components, drive engineering standards, and partner closely with quant, trading, risk, and production management teams.
Job responsibilities
Build real-time Risk/PnL systems supporting Loans, Credit Derivatives, Exotics products etc (e.g., intraday Greeks/sensitivities, VaR inputs, explain/attribution, scenario and stress runs).
Design and deliver low-latency, high-throughput services that publish risk and PnL to front-office consumers with clear SLAs, observability, and operational readiness.
Develop distributed microservices and event-driven pipelines that consume market data, trades, and reference data; produce risk measures; and serve APIs/UI consumers.
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.
Own technical design and implementation with awareness of upstream/downstream dependencies, data contracts, schema evolution, and failure modes.
Apply strong engineering rigor: test strategy, performance profiling, capacity planning, resiliency patterns, and secure coding.
Drive production excellence: incident triage, root cause analysis, runbooks, automated recovery, and measurable reliability improvements.
Collaborate with stakeholders to translate business needs into clear technical requirements and deliver iteratively.
Mentor engineers, contribute to code reviews, raise the bar on architecture and craftsmanship, and foster an inclusive team culture.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 12+ years applied experience
Extensive hands-on experience delivering Python services in production (design, development, testing, troubleshooting, and operational support).
Strong knowledge of data structures, algorithms, concurrency, and software design principles; able to lead design discussions and document architecture.
Experience across the full SDLC: CI/CD, testing automation, release management, and production support.
Strong SQL skills and experience with relational databases; ability to design schemas and write performant queries.
Proven ability to build secure, stable, maintainable systems in a large enterprise environment (controls, auditability, SDLC governance).
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
Experience building real-time systems: messaging, streaming, caching, and low-latency APIs.
Proficiency with profiling and performance tuning (CPU/memory/IO), and designing for throughput, backpressure, and graceful degradation.