Lead Software Engineer (VP) - Valuation Control Group Technology

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 (VP) - Valuation Control Group Technology at JPMorganChase within the Valuation Control Group 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.

Lead end-to-end architecture for Athena (Python), Databricks (PySpark/Delta Lake), and AWS-based valuation control platforms. Develop and enhance pricing, risk, IPV, valuation adjustment, and fair value reporting tools within Athena. Design, build, and optimize large-scale Databricks ETL and analytics pipelines for pricing, market, and P&L data. Architect and manage cloud-native AWS solutions, including infrastructure, security, automation, and modernization initiatives. Establish engineering best practices, code quality standards, CI/CD processes, testing frameworks, and production monitoring. Deliver automation and controls for independent price verification, valuation uncertainty detection, reconciliations, and regulatory compliance. Collaborate with valuation control, risk, finance, quants, and business stakeholders to define requirements and drive Agile delivery. Provide technical leadership, mentor engineering teams, oversee production support, and ensure scalable, auditable, and resilient solutions. 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 Extensive Python engineering experience; with hands-on platform experience and financial application engineering (derivatives pricing, risk management, trade booking, and quantitative analytics etc.). Strong Databricks / Apache Spark experience (PySpark, Delta Lake, performance tuning). Solid hands-on AWS experience across core services (S3, EC2, Lambda, IAM, and container/orchestration services), with an understanding of cloud security and cost management. Solid grasp of financial products, valuation/pricing concepts, and market data. Proven experience leading engineering teams and delivering in a controlled, regulated environment. Strong SQL, data modeling, and data-quality engineering skills. Excellent stakeholder management and the ability to bridge business and technical domains.

Knowledge of IPV, prudent valuation, XVA, or fair-value reserve methodologies. Experience with reactive/streaming computation and real-time dashboards. Infrastructure-as-code (Terraform/CloudFormation), CI/CD, and modern DevOps practices on AWS. AWS certification (e.g., Solutions Architect, Developer, or Data Engineer Associate/Professional).