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 at JPMorgan Chase within the Commercial and Investment Bank, 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. You will provide technical direction across a complex analytics portfolio, strengthen the team through developer mentorship, and advance a roadmap for trusted data, modern analytics, and continuous engineering improvement. 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 • Lead design, development, optimization, and maintenance of complex, highly visible Tableau dashboards; guide architecture, performance, usability, and quality. • Deliver and oversee embedded Tableau dashboards and both packaged (.twbx) and un-packaged (.twb) workbooks. • Explore varied datasets for trends, outliers, and relationships; build interactive visualizations that let stakeholders drill into details and discover insights. • Mentor developers through technical guidance, design and code reviews, standards, and collaborative problem-solving. • Partner with technology teams, application owners, and data management teams on a continuous-improvement roadmap, including advanced analytics and cloud adoption. • Lead data integration across warehouses, SQL and NoSQL databases, and APIs; use Python and automation to improve reliability and delivery. • Promote effective GitHub Copilot use and practical AI-assisted development integration techniques within team workflows. • 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. • Define, develop, monitor, and report key management metrics that show the health and performance of a global operations program. • Partner with senior leaders to advance analytics and deliver improvements in operational efficiency, cost modeling, capacity planning, quality, and related areas. • Define, design, and manage high-priority strategic initiatives with senior-level visibility, clear outcomes, governance, milestones, and delivery plans. Required qualifications, capabilities, and skills • Formal training or certification on software engineering concepts and 8+ years applied experience • Expert hands-on Tableau experience, including complex dashboard design, development, troubleshooting, performance tuning, and production support. • Knowledge of analytical and BI tools, including Tableau and Qlik Sense and Grafana • Project experience using SQL, Qlik Sense, Tableau, Python, R, and Alteryx or comparable tools. • Strong knowledge of data warehouses, data modeling, and connections to diverse databases, data types, and data structures. • Extensive API experience, including designing, consuming, and troubleshooting REST APIs and BI platform integrations. • Python experience in analytics, data processing, automation, or BI development; experience with GitHub Copilot and AI-assisted development integration techniques. • Ability to lead a complex analytics portfolio and backlog, manage competing priorities, and deliver high-priority initiatives with measurable outcomes. • Experience mentoring developers and partnering with senior leaders, business stakeholders, technology teams, application owners, and data management teams. • 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 Preferred qualifications, capabilities, and skills • 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