The role
From JPMorgan Chase's own posting.
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