VP Product Management, Intelligent Data Layer

LPL Financial•Fort Mill, SC

5d•$135,960 - $226,600

About The Position

LPL is building the data foundation that enables AI across the firm. The VP Product Management, Intelligent Data Layer will transform business information into reusable data products, shared business definitions, semantic models, and knowledge retrieval capabilities that support accurate and useful AI applications. This leader owns the portfolio’s product strategy, roadmap, and lifecycle, leads a team of product managers, and partners with engineering, data science, AI application teams, and business stakeholders. The role grounds product development in defined business use cases by establishing an end-to-end path from source information through its use in an AI application. This “steel thread” approach validates capabilities in production-oriented use cases and informs reusable data, semantic, and retrieval products for additional applications across the firm. The position also establishes product quality, governance, and evaluation measures, and connects product investments to business outcomes.

Requirements

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.

Nice To Haves

Ability to resolve competing business requirements and communicate product decisions to technical teams and senior leaders.
Financial services or other regulated-industry experience.

Responsibilities

Define and manage the Intelligent Data Layer product strategy, roadmap, priorities, and investment decisions, balancing business value, reuse, delivery effort, operating cost, and technical dependencies.
Lead development and lifecycle management of reusable data products that make business facts, relationships, measures, and contextual information available to AI applications.
Establish semantic-layer capabilities that translate agreed business definitions into reusable data models, relationships, metrics, and calculations for consistent use across applications.
Lead knowledge and retrieval product capabilities, including enterprise search, knowledge graphs, retrieval-augmented generation (RAG), and GraphRAG, and evaluate performance against defined business questions and use cases.
Partner with AI application, engineering, data science, and business teams to deliver end-to-end use cases from source data through application consumption, using implementation lessons to expand reusable platform capabilities.
Own the product lifecycle from discovery and requirements through launch and ongoing improvement, defining 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, and use results to prioritize improvements.
Hire, develop, and lead product managers; establish delivery commitments with engineering leaders; and manage cross-functional dependencies and priorities.
Establish governance and ownership models with business and technology partners for shared definitions, semantic models, data products, and knowledge assets.
Measure adoption, product quality, reliability, and operating cost, and partner with application owners to evaluate business outcomes and inform roadmap investments.