Wealth Management - Data Provisioning Execution & Innovation - Vice President

Execution & Innovation Lead

Our Chief Data & Analytics Office within Asset & Wealth Management is reimagining how data is provisioned, governed, and consumed. Through Strategic Data Provisioning, we organize data into domain-aligned products, build consumer-ready unified data products, and pioneer a human-agentic operating model for data management. We are looking for a curious, self-directed innovator ready to shape the future of AI for Data.

Job Summary

As the Execution & Innovation Lead within our Strategic Data Provisioning team, you will spend every day connecting the work happening on the ground with the AI for Data vision shaping tomorrow's capabilities. Translating granular data flows, producer dependencies, and consumer needs into pragmatic, sequenced requirements, you will partner across knowledge base owners, agentic orchestration product owners, applied AI delivery leads, and use case execution teams. If you are intellectually curious and have a passion for delivering solutions across organizational boundaries, you may be the perfect fit for our team.

Job Responsibilities

• Partner with use case owners and execution teams to take requests from problem space to provisioned, governed data products on the Mesh
• Engage producers and Systems of Record directly to action the SDP ticket backlog, with deliberate focus on the CDO "Big Rocks" subset
• Break down priority lists, sequence producer conversations, and track dependencies needed to onboard additional data to the Mesh
• Provide executive visibility on bottlenecks, cross-product dependencies, and adoption progress against SDP OKRs
• Support lineage and provenance work for critical data assets in alignment with regulatory and control obligations, ensuring an evergreen, auditable record
• Lead aggregate cross-use case root cause analysis on priority data quality issues using profiling and analytics, and guide fixes at source
• Partner with control owners to embed preventative technical and business data quality controls so failures are prevented rather than re-detected
• Enrich the metadata, classification, and discoverability of existing data assets so they become more valuable to consumers, including downstream NLQ and AI use cases
• Translate ground-level patterns, friction points, and producer dependencies into clearly articulated requirements for the agentic approach
• Contribute the SDP perspective on knowledge curation, semantic enrichment, and lifecycle orchestration so platform capabilities reflect real demand
• Maintain a continuous feedback loop between demand signals and the capability roadmap, surfacing executive views of risks, dependencies, and decisions needed

Required Qualifications, Capabilities, and Skills

• Bring 8 years in data management or operations transformation within financial services, including VP-level delivery in matrixed environments
• Apply deep subject matter knowledge across wealth and asset management data domains, including investments
• Deliver or partner on analytics enablement transformation programs with a proven track record
• Translate operational, ground-level work into structured requirements that other teams can build against
• Engage producers, Data Executives, and delivery partners credibly using a strong technical foundation
• Code and analyze using Alteryx, Python, SQL, and Spark across cloud data platforms
• Influence senior stakeholders and operate effectively in a partner role where delivery sits with others
• Balance long-term vision with pragmatic, incremental delivery through thoughtful judgment
• Sequence complex producer and consumer dependencies to unblock active use cases
• Communicate executive-level views of risks, dependencies, and decisions to senior stakeholders
• Collaborate across organizational boundaries as an enthusiastic, self-directed team member

Preferred Qualifications, Capabilities, and Skills

• Demonstrate familiarity with Data Mesh patterns and capabilities
• Apply a conceptual understanding of AI for Data approaches, including how knowledge curation and agentic orchestration intersect with traditional data management
• Recognize telemetry, continuous-optimization, and cost/accuracy/predictability tradeoffs in agentic systems (e.g., tokenomics-style framing) sufficient to inform requirements
• Partner alongside AI or analytics transformation product teams in a requirements capacity
• Program and analyze using Python, R, SQL, and PySpark
• Build and present insights using Tableau, Power BI, or equivalent visualization tools
• Operate within Agile/Scrum, DevOps, and DataOps methodologies using Git, GitHub, or Bitbucket