Data Visualization Manager, Vice President

JPMorgan Chase & Co.•Columbus, OH

11h

About The Position

Have an opportunity to redefine how a major Finance organization makes decisions — and grow your career while you do it. You will combine best-in-class business intelligence with applied artificial intelligence to deliver insights that reach the most senior leaders. You will move the team beyond static dashboards toward natural-language, decision-ready experiences. This is a high-visibility, high-autonomy role for a hands-on technical leader who wants to set standards and build something durable. It begins as a senior individual contributor position and is designed to grow into formal people leadership as the portfolio scales. As a Data Visualization Manager, Vice President on the Wealth Management Finance Data & Insights team, you will deliver executive-grade insights and artificial-intelligence-enabled experiences that help our most senior Finance leaders make faster, higher-confidence decisions. You will own the full lifecycle of our visualization and conversational analytics products from framing the problem and modeling the data to designing the experience, deploying it, and driving adoption. You will build guided self-service and natural-language tools that let leaders explore performance drivers, scenarios, and risk-and-return trade-offs in plain language. You will partner closely with Finance, Product, Data Engineering, and platform teams to keep everything secure, compliant, and production-grade. Most importantly, you will translate leadership questions into trusted data products and high-performing experiences. You will modernize the team's analytics delivery, partnering across Finance, Product, Data Engineering, and platform teams to ensure solutions are secure, compliant, and production-grade. You will personally drive the technical translation from leadership questions into governed data products and performant experiences.

Requirements

Experience delivering data solutions and analytics for Finance stakeholders.
Demonstrated production delivery in Tableau and/or ThoughtSpot, including dashboard design and metric architecture.
Hands-on experience with the Databricks platform, including contributing to technical design discussions on semantic modeling and performance.
Advanced SQL skills and the ability to validate datasets and logic end-to-end.
Demonstrated ability to translate business questions into analytical products, including requirements definition and acceptance criteria.
Experience building and maintaining a backlog independently based on stakeholder needs.
Demonstrated experience setting quality standards and mentoring analysts.
Demonstrated risk and control practices in a regulated environment, including data access governance and change management for reporting.

Nice To Haves

Experience building or product-managing conversational analytics or natural-language query experiences, including quality evaluation and guardrails.
Experience with Databricks Genie and/or ThoughtSpot Spotter to enable governed self-service.
Proficiency with Alteryx for workflow automation and repeatable data preparation.
Wealth Management finance domain knowledge (profitability drivers, advisor metrics, client flows, assets under management, pricing/fees, and forecasting).
Experience establishing analytics governance, such as metric-definition standards, release management, and adoption measurement.
Familiarity with model risk management and artificial-intelligence governance in regulated environments.
Prior formal people-management experience (hiring, performance management, and team development).

Responsibilities

Influence the analytics product portfolio for Wealth Management Finance, including roadmap, backlog, prioritization, and value delivery.
Design and deliver executive-grade dashboards and narratives in ThoughtSpot, Databricks, and Tableau, aligned to governed metrics and finance definitions.
Build artificial-intelligence-enabled conversational analytics using Databricks Genie and ThoughtSpot Spotter, including interaction design, evaluation, and monitoring.
Partner with Data Engineering to ensure data quality, timeliness, lineage, and scalable architecture, validating semantic models and metric logic hands-on.
Implement controls suited to a regulated environment, including access controls, privacy-by-design, and alignment to model, data, and operational risk expectations.
Drive adoption through enablement, training, documentation, and measurable usage tracking with clear leadership feedback loops.
Provide strong executive presence in senior Finance forums, framing insights, drivers, and recommended actions concisely.
Establish technical and delivery standards and mentor analysts and visualization specialists through structured coaching and review.