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VP Product Management, Intelligent Data Layer
LPL Financial. Define and manage the Intelligent Data Layer product strategy, roadmap, priorities, and investment decisions .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates extensive experience in product management for enterprise data products and AI capabilities, with a strong focus on semantic modeling, knowledge graphs, and product lifecycle management. Proven ability to lead cross-functional teams, establish governance models, and make strategic product investment decisions.
Highest-signal resume keywords
Product Management ExperienceSemantic ModelingKnowledge GraphsAI Evaluation MethodsCross-Functional Team Leadership
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Product StrategyData Product DevelopmentLifecycle ManagementData AccuracyRetrieval-Augmented Generation
Soft Skills
CommunicationDecision-MakingTeam Leadership
Tools & Technologies
Enterprise SearchGraphRAG
Industry Keywords
Financial ServicesRegulated Industry
About the role
Key responsibilities & impact- Define and manage the Intelligent Data Layer product strategy, roadmap, priorities, and investment decisions
- Lead development and lifecycle management of reusable data products for AI applications
- Establish semantic-layer capabilities, reusable data models, relationships, metrics, and calculations
- Lead knowledge and retrieval capabilities, including enterprise search, knowledge graphs, RAG, and GraphRAG
- Partner with AI application, engineering, data science, and business teams on end-to-end use cases
- Own the product lifecycle from discovery and requirements through launch and ongoing improvement
- Define product requirements for accuracy, data freshness, access permissions, source traceability, reliability, and support
- Establish product quality and AI evaluation measures for retrieval relevance, answer grounding, data accuracy, reliability, adoption, latency, and operating cost
- Hire, develop, and lead product managers
- Establish delivery commitments with engineering leaders and manage cross-functional dependencies and priorities
- Establish governance and ownership models for shared definitions, semantic models, data products, and knowledge assets
- Measure adoption, product quality, reliability, and operating cost to inform roadmap investments
Requirements
What you’ll need- 10+ years of relevant product management experience, including experience with enterprise data products and production AI capabilities
- Experience leading product teams and delivering products across engineering, data science, and business organizations
- Experience taking data or retrieval products into production, supporting multiple consumers, and improving product performance following launch
- Working knowledge of semantic modeling, knowledge graphs, retrieval-augmented generation (RAG), and AI evaluation methods
- Experience making product investment, build-versus-buy, and technical tradeoff decisions
- Ability to resolve competing business requirements and communicate product decisions to technical teams and senior leaders
- Financial services or other regulated-industry experience
Benefits
Comp & perks- 401K matching
- Health benefits
- Employee stock options
- Paid time off
- Volunteer time off
- Competitive LPL Total Rewards package