Senior Lead Software Engineer - Data & Analytics

We are looking for a curious, impact-driven engineer who thrives at the intersection of data, product, and platform — someone who can turn complex engineering signals into clear, compelling insights that drive decisions at scale. At JPMorganChase, we invest in the tools and talent that make great engineering possible, and this role sits at the heart of that mission. As a Senior Lead Software Engineer at JPMorganChase within the Engineering Efficiency and Analytics team, you will design and deliver a firmwide metrics and analytics platform that brings visibility to engineering performance, delivery health, and the measurable impact of AI-assisted development. You will work end-to-end — from data pipelines and backend services to modern web dashboards — partnering with engineering leaders and teams across the firm to define what "good" looks like and make it visible. This is a broad, creative, T-shaped engineering role where your technical depth, analytical thinking, and storytelling ability will directly shape how the firm understands and improves its engineering capability. Job responsibilities Design and implement modern web dashboards and end-to-end workflows that transform engineering data into clear, actionable insights for teams and leaders across the firm Build and operate reliable data pipelines that ingest, transform, validate, and publish metric datasets to power analytics experiences, integrating with platforms such as Databricks as needed Design, build, and maintain secure, scalable backend services and REST APIs that enable metric consumption and power the user interface Define and validate metric logic, perform exploratory and trend analysis, and translate findings into compelling dashboard narratives that inform engineering decisions Partner with engineering teams, leaders, and stakeholders to align on performance benchmarks and translate shared goals into a coherent metrics strategy Implement CI/CD pipelines, automated testing, secure coding standards, and code review practices to deliver production-grade, maintainable software Deploy and operate services on cloud and container platforms, ensuring reliability, scalability, and operational excellence Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards 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 advanced applied experience Full-stack engineering capability with demonstrated experience building dashboards and web experiences end-to-end, spanning frontend, back-of-frontend, and backend integration Experience working with SQL databases and analytical data stores at scale, including data warehouse concepts and query optimization Demonstrated engineering practices including CI/CD pipeline implementation, automated testing, and code review standards Analytical capability to develop and validate metrics — including exploratory analysis, cohort and trend analysis, and anomaly detection — and translate results into clear visual narratives Effective communication and stakeholder partnership skills, with the ability to align technical and non-technical audiences around shared goals Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs Preferred qualifications, capabilities, and skills Experience with React and TypeScript building performant, accessible, data-rich dashboards and visualizations Backend service and REST API development experience, including authentication and authorization, versioning, error handling, and performance optimization Experience deploying and operating services on cloud or container platforms (e.g., AWS, Kubernetes, or equivalent enterprise platforms) Experience integrating with Databricks to produce curated, dashboard-ready datasets, including layered data product patterns (e.g., bronze/silver/gold or equivalent) with clear contracts and quality controls Hands-on experience with AWS services (e.g., ECS, S3, SQS, SNS, Kafka), PostgreSQL, and infrastructure-as-code tools such as Terraform Prior experience building metrics or insight products for engineering productivity, developer experience, or technology transformation programs Interest or experience in UX and data visualization design principles