Lead modernization of pricing data platforms for fixed income and derivatives. Re-engineer data pipelines, storage, and configurable client delivery across on-premises and AWS environments. Build and support production systems using Python, SQL, and NoSQL; troubleshoot incidents and perform root-cause analysis. Lead engineers, collaborate with senior stakeholders, and promote governed AI-assisted development practices, including secure code review, testing, automation, and validation of AI outputs.
Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.
As a Senior Lead Software Engineer - Python, SQL, NoSQL at JPMorgan Chase within the Commercial & Investment Bank - Pricing Direct team, you'll be 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. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.
Job responsibilities
- Re-engineer and modernize the pricing data processing and client-delivery platform for Fixed Income and Derivative pricing using contemporary development and data pipeline practices.
- Refactor and streamline end‑to‑end data flows; optimize data and file storage; enable configurable delivery formats for diverse client needs.
- Design and implement a strategic platform spanning on‑prem and cloud-native AWS services, with a focus on scalability, resilience, and security.
- Maintain and enhance existing pricing data pipelines to improve reliability, performance, and supportability.
- Provide application support to business and client-support teams, including troubleshooting, incident resolution, and root-cause analysis.
- Represent the function in cross functional forums and collaborate with senior stakeholders.
- Drive adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
- Apply knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience.
- In addition, 2 + years of experience leading technologists to manage and solve complex technical items within your domain of expertise
- Strong, current hands‑on Python development experience building and supporting production systems.
- Solid understanding of relational SQL and NoSQL databases, data modeling, and data structures.
- Ability to analyze and maintain and re-engineer existing tools
- Proven debugging and problem‑solving skills; comfort supporting mission‑critical data flows.
- Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls.
- Leverage AI-powered development tools (Copilot, Claude, Codex) to accelerate code generation, review, testing and delivery across the software lifecycle
- Experience designing hybrid architectures across on‑prem and AWS
Preferred qualifications, capabilities, and skills
- Unix and Perl scripting
- Background in data pipeline orchestration and optimization (batch or streaming), and configurable data delivery.
- Familiarity with software engineering best practices (version control, testing, CI/CD), observability (logging/monitoring), and performance tuning.
- Familiarity with Fixed Income and derivatives products and pricing processes (curve construction, instrument conventions, valuation metrics).
- Design, build, and deploy scalable cloud-native applications and services on AWS using modern infrastructure and DevOps practices.
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