VP, Product Management - Data Quality

LPL Financial•Fort Mill, SC

7d•Hybrid

About The Position

What if you could build a career where ambition meets innovation? At LPL Financial, we empower professionals to shape their success while helping clients pursue their financial goals with confidence. What if you could have access to cutting-edge resources, a collaborative environment, and the freedom to make an impact? If you're ready to take the next step, discover what’s possible with LPL Financial. Job Overview: The VP, Product Management - Data Quality will serve as the product leader for LPL's data quality, capabilities and solutions. This role bridges the gap between governance strategy and technical execution by developing and managing the roadmap for core governance capabilities including data catalog, lineage, reconciliation, data quality, cataloging, profiling, and incident response. Reporting to the Senior Vice President of Data Governance & Responsible AI, this leader will work closely with engineering teams to translate governance requirements into technical solutions. The role requires a unique blend of data governance expertise and product management skills to prioritize development efforts, manage backlogs in JIRA, and ensure governance tools deliver measurable value to the organization. This leader will champion data quality standards and practices across the enterprise, driving adoption through training, enablement, and continuous improvement of governance capabilities. The ideal candidate thrives at the intersection of business needs and technical delivery, with a passion for building scalable, user-friendly governance solutions. This role offers a unique opportunity to drive the execution of critical data governance capabilities—including innovative AI-powered solutions—and contribute directly to LPL Financial’s data excellence initiatives.

Requirements

A bachelor's or master's degree in Computer Science, Information Systems, Data Science, Business, Engineering, or a related field.
At least 10 or more years of experience in data quality data management, or data platform development, with a proven track record of delivering solutions.
Minimum 5 or more years of experience in product ownership or product management roles, preferably with technical products or platforms.
Product Management: Proven experience managing technical products with demonstrated ability to balance business needs, technical constraints, and user experience.
Data Governance Expertise: Deep knowledge of data governance frameworks, including cataloging, lineage, quality, reconciliation, profiling, and metadata management.
Agile Development: Strong understanding of agile methodologies, JIRA, and software development lifecycle with ability to work effectively with engineering teams.
Technical Acumen: Familiarity with data platforms, cloud architectures, and governance tools such as Collibra, Atlan, Alation, AWS DataZone, or similar solutions.
AI and Machine Learning Acumen: Experience developing or managing AI-driven data solutions, including natural language interfaces and advanced lineage tracing.
Knowledge of Modern Data Architectures: Familiarity with technologies such as Vector Stores, Knowledge Graphs, and RAG architectures.
OpenLineage Experience: Experience with OpenLineage or similar open-source lineage frameworks is a plus.
Stakeholder Management: Excellent ability to communicate with both technical and non-technical audiences, building consensus across diverse stakeholder groups.
Analytical Thinking: Strong problem-solving skills with ability to translate complex governance requirements into actionable development priorities.
Change Management: Experience driving adoption of new tools and processes through effective communication, training, and enablement strategies.
Regulatory Awareness: Understanding of financial services regulations and their impact on data governance, privacy, and security.
Leadership: Ability to influence without direct authority, motivating cross-functional teams toward shared governance goals.
Detail-Oriented: Meticulous attention to detail in documentation, policy development, and quality assurance.

Nice To Haves

Experience working directly with engineering teams using agile methodologies and tools such as JIRA, Confluence, or similar platforms.
Experience designing, implementing, or managing AI-powered data quality solutions (e.g., natural language metadata search, automated lineage tracing).
Familiarity with OpenLineage or similar open-source data lineage frameworks is highly desirable.
Demonstrated experience developing and managing product roadmaps for data or analytics capabilities.
Strong understanding of data governance frameworks and best practices (DCAM, Dama DMBOK), including hands-on experience with governance tools and technologies.
Experience in financial services or other highly regulated industries preferred.
Track record of driving adoption and change management for technical solutions.
Experience with cloud data platforms (AWS, Azure, GCP) and modern data architectures.
Familiarity with metadata management and data quality tools such as Collibra, Alation, Atlan, Informatica, or AWS DataZone a plus.

Responsibilities

Data Quality Product Ownership: Define and manage the product roadmap for data governance capabilities including, data quality, profiling, and incident response systems.
Technical Collaboration: Partner with engineering and data platform teams to translate data quality requirements into technical specifications, user stories, and acceptance criteria.
AI-Driven Solution Development: Lead the design and implementation of AI-powered data quality tools, such as the Data Quality GPTs, which enable users to interact with metadata using natural language for faster system understanding, field mapping and SQL query generation.
Advanced Data Lineage Architectures: Oversee the development of next-generation data quality solutions leveraging Vector Stores, Knowledge Graphs, and Retrieval-Augmented Generation (RAG) architectures to enhance traceability and transparency across data systems.
Backlog Management: Maintain and prioritize JIRA backlogs for governance development initiatives, ensuring alignment with strategic priorities and stakeholder needs.
Capability Development: Drive the build-out of governance tools and platforms, making strategic decisions between build, buy, and integration approaches.
Standards & Policy Management: Develop, document, and promote data quality standards, policies, and best practices across the organization.
Stakeholder Engagement: Collaborate with data stewards, business units, and compliance teams to understand requirements and ensure governance solutions meet operational needs.
Metrics & Monitoring: Establish and track KPIs for data quality capability adoption and incident resolution performance.
Training & Enablement: Create and deliver training programs to promote awareness and adoption of governance tools and standards.
Continuous Improvement: Gather user feedback, analyze usage patterns, and iterate on governance capabilities to enhance user experience and effectiveness.
Vendor Management: Evaluate and manage relationships with governance tool vendors, ensuring optimal performance and value delivery.
Documentation: Maintain comprehensive documentation for governance processes, tools, and standards to support audit readiness and knowledge transfer.