LPL Financial is looking to shape the future of its data governance and contribute to the organization's strategic goals by developing and executing enterprise-wide governance strategies for both data and AI.
Requirements
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: Proven ability to develop and enforce enterprise-wide data and AI policies.
Regulatory Acumen: Familiarity with financial services regulations, especially those impacting data privacy, security, and AI usage.
Experience with DataZone from AWS a plus
Strong financial acumen with a track record of effectively managing budgets exceeding $50 million.
Experience leading teams through significant transformations, particularly in migrating from legacy systems to modern cloud architectures.
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.
Other
A bachelor's or master's degree in business, Computer Science, Information Systems, Data Science, Engineering, or a related field.
At least 15 or more years of experience in Data & Analytics product management or equivalent experience, including a proven track record of leading significant successful data transformation initiatives in complex environments.
Minimum 5 or more years of experience in data platform and services build-out in partnership with engineering teams and data product ownership in a mid to large scale enterprise.
Proven experience in a senior leadership role, preferably at the SVP level.
Exceptional communication and stakeholder management skills.
