VP, Data Product Owner — Advisor System of Record (Advisor SOR)

LPL Financial•Charlotte, TX

About The Position

Where Ambition Meets Innovation Build a career that matches all your initiative with an impressive dose of innovation. From cutting-edge resources and a collaborative environment to the freedom to make an impact and more, you’ll find the ingredients you need at LPL Financial to shape your success while helping clients pursue their financial goals. Job Overview: LPL Financial is seeking a VP, Data Product Owner for the Advisor System of Record (Advisor SOR) to own the end-to-end product lifecycle of advisor data, including canonical modeling, data contracts, quality, lineage, and consumer adoption—enabling a trusted, scalable foundation for advisor lifecycle processes, AI enablement, and enterprise transformation. This role is the single-threaded product owner for the Advisor System of Record (SOR)—one of LPL’s most critical enterprise data assets.The VP will translate enterprise advisor lifecycle strategy into delivered, production-grade data products, partnering across Product, Technology, and Governance.

Requirements

10–15+ years in data, product, or domain roles within Wealth Management or Financial Services
7 plus years experience owning or delivering enterprise data products or systems of record
Experience with: Domain-driven design Data product operating models (DPO frameworks) Metadata/catalog and observability tools
Deep understanding of Wealth Management advisor data, including: Advisor identity, hierarchy, and book of business Relationships to clients, accounts, positions, and transactions
Strong knowledge of: Data modeling (conceptual, logical, canonical) Data contracts and APIs Data quality, lineage, and reconciliation Modern data platforms and integration patterns
Hands-on or strong working knowledge of: Cursor (AI-assisted development workflows) GitHub (version control, CI/CD, collaboration) Anthropic tools (e.g. Claude) for prompt engineering, agent workflows, and productivity
Familiarity with: AI/ML data requirements Feature engineering and semantic layers Agentic workflow design and automation patterns
Owns a critical enterprise data product with direct business and regulatory impact
Applies scalable, modern solutions including AI-enabled approaches
Drives alignment across product, tech, governance, and business stakeholders
Combines WM domain expertise with modern data and AI capabilities
High-ownership execution leader with strong cross-functional influence

Nice To Haves

Exposure to: M&A integrations or platform consolidations AI/ML-enabled products or automation initiatives

Responsibilities

Define and execute the Advisor SOR product vision, roadmap, and backlog
Establish canonical advisor data models, hierarchies, and relationships
Define and manage: Data contracts (schema, SLAs, versioning) Critical data elements (CDEs) Data quality SLOs
Serve as the authoritative decision-maker for advisor data definitions and product scope
Implement scalable frameworks for: Data quality monitoring and remediation Reconciliation and defect management Lineage and impact analysis
Partner with Data Governance to ensure: 100% catalog coverage of critical advisor attributes Policy-to-control alignment Audit and regulatory readiness
Ensure Advisor SOR is optimized for AI and advanced analytics use cases
Partner with AI Business Solutions and Data Science teams to: Define feature-ready datasets and semantic layers Support AI agents and copilots with high-quality advisor data
Leverage modern AI-enabled development tools, including: Cursor (AI-assisted coding and development workflows) GitHub (code management, CI/CD, and collaboration) Anthropic tools (Claude-based workflows, prompt engineering, agent design)
Contribute to development of agentic workflows and AI-driven automation across advisor lifecycle processes
Lead hydrationn of advisor data into Strategic SOR platforms
Partner with Technology to: Build scalable data pipelines, APIs, and distribution layers Ensure platform performance, reliability, and scalability
Support M&A and migration initiatives, including: Source-to-target mapping Mock conversions and validation Cutover readiness and reconciliation
Identify and onboard priority consumers: Advisor platforms Operations workflows AI/analytics use cases
Develop: Migration and onboarding playbooks Legacy decommission strategies
Partner with Data Distribution and consumer teams to ensure seamless adoption
Design and implement scalable patterns: Data contracts Automated quality frameworks Reusable lineage and reconciliation models
Contribute to AI-enabled product innovation and workflow automation
Familiarity with FINRA, SEC, and privacy requirements impacting advisor data
Senior individual contributor with high ownership and decision authority
May manage a small team (1–3 resources), but primary focus is product ownership and delivery
Leads cross-functional initiatives impacting multiple systems and teams
Accountable for delivery of a critical enterprise data product