Senior Vice President, Data & AI Governance and Tooling Lead

Job Description

LPL Financial (Nasdaq: LPLA) was founded on the principle that the firm should work for the advisor, and not the other way around. LPL Financial is one of the nation's leading financial services companies and a publicly traded company under ticker symbol LPLA. LPL is a fast-growing, fortune 500 company with 7000+ employees.  We support over 50,000 financial professionals—including over 20,000 independent advisor teams, more than 800 financial institutions, and over 4,000 institutional clearing and technology subscribers managing over USD 1.2 trillion in assets.

At LPL, independence means that advisors have the freedom they deserve to choose the business model, services, and technology resources that allow them to run their perfect practice. And they have the freedom to manage their client relationships because they know their clients best. Simply put, we take care of our advisors, so they can take care of their clients.

This role offers a unique opportunity to shape the future of LPL Financials’ data governance and contribute to the organization's strategic goals. 

Overview

The Senior Vice President of Data & AI Governance and Tooling will lead the development and execution of enterprise-wide governance strategies for both data and AI. This role supports LPL’s evolution into a hybrid “build and buy” AI organization by ensuring governance is embedded across data assets, AI models, and operational workflows.

Reporting to the Chief Data & AI Officer, this leader will scale governance capabilities—including data cataloging, quality, lineage, reconciliation, and documentation—while also establishing robust AI governance frameworks. The role requires close collaboration with data, technology, operations, legal, and compliance teams to align governance with business goals and regulatory expectations.

This leader will also drive adoption of governance tools, foster data curiosity, and ensure teams are equipped to use governance dashboards and insights to support decision-making and innovation.

Key Responsibilities

• AI Governance: Define and manage governance for AI/ML and GenAI models, covering lifecycle oversight, risk, explainability, bias, and performance.
• Policy Leadership: Develop and enforce enterprise-wide data and AI policies, including Partial Data Masking and Data Retention, integrated into platforms and workflows (e.g., ServiceNow).
• Regulatory Alignment: Ensure governance practices meet or exceed FINRA and other regulatory standards; proactively adapt to changes.
• Tooling Strategy: Expand use of AI-driven tools for metadata tagging, lineage, documentation, and reconciliation.
• Governance Reporting: Build dashboards and metrics to track AI model governance, policy compliance, and audit readiness.
• Cross-Functional Collaboration: Partner with engineering, data science, legal, and compliance to embed governance into development and operations.
• Data Governance Delivery: Scale core capabilities—cataloging, quality, lineage, reconciliation, and documentation—using a mix of build, buy, and AI solutions.
• Change Management: Lead adoption strategies and training to embed governance into daily workflows across teams.
• Team Leadership: Mentor and grow a high-performing team of data professionals.
• Continuous Improvement: Refine governance practices based on feedback, performance data, and industry trends.

KPIs & Success Metrics

• AI Governance
  • % of AI models reviewed and approved through governance processes
  • % of models with explainability and bias assessments completed
• Policy Compliance
  • % compliance with data retention and masking policies
  • Reduction in regulatory audit findings related to data/AI governance
• Tooling Adoption & Data Quality
  • % of business units actively using governance tools (catalog, lineage, reconciliation)
  • Reduction in critical data quality issues over time
  • % of assets with complete and current metadata
• Operational Efficiency
  • Mean time to resolve data-related incidents (via ServiceNow)
  • % of critical data domains governed
• Training & Enablement
  • Number of users trained
  • Satisfaction scores from enablement sessions

Key Competencies

• Data Governance: Expert in data governance, specifically cataloging, quality, lineage, reconciliation and documentation of data assets.
• AI Risk & Ethics: Deep understanding of responsible AI principles and regulatory trends.
• Policy Leadership