AVP, Data Product Owner – Wealth Management (Data Products)

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• *AVP, Data Product Owner – Wealth Management (Data Products)**

• *Location**

Hybrid / Onsite as required

• *Reports To**

VP / Strategic Data Lead

• *Role Summary**

LPL Financial is seeking an **Assistant Vice President, Data Product Owner (DPO)** to own and manage individual Wealth Management (WM) data products. This role is responsible for **day\-to\-day ownership, execution, and governance** of assigned datasets, ensuring they are accurate, usable, and aligned with business needs.

The AVP DPO operates as the **hands\-on product owner**, working closely with engineering, data stewards, and business stakeholders to define requirements, manage data quality, and drive adoption.

This is **not a reporting or analytics role** — it is an **execution\-focused data product ownership role** with clear accountability for data quality, usability, and governance.

• *Key Objectives**

• Establish **clear ownership for assigned WM data products**
• Deliver **well\-defined, governed, and consumable datasets**
• Improve **data quality and trust at the product level**
• Embed governance into **delivery workflows and backlog execution**
• Support enterprise initiatives with **high\-quality data products**

• *Core Responsibilities**

• *1\. Data Product Ownership \& Execution**

• Act as the **named Data Owner for specific WM data products**
• Define and maintain:

+ Business definitions and metadata

+ Use cases and data consumers

+ Data quality rules and thresholds

* Make decisions on

+ Fitness\-for\-purpose for assigned datasets

+ Data access approvals (within defined guardrails)

+ Prioritization of enhancements within backlog

• *2\. Wealth Management Domain Support**

* Own data products within WM domains such as

+ Clients / Participants

+ Accounts and transactions

+ Securities and pricing

• Serve as **primary point of contact for consumers**
• Ensure data supports operational and regulatory use cases

• *3\. Data Product Lifecycle Management**

Execute lifecycle activities

Define Build Publish Operate* + Enhance

* Maintain artifacts

+ Data Product Charter and Canvas

+ Product backlog and roadmap

* Partner with delivery teams to ensure

+ Requirements are clear and actionable

+ Data considerations are embedded in delivery

• *4\. Data Quality \& Issue Management**

• Identify and track **Critical Data Elements (CDEs)**
• Define data quality rules with Data Stewards
• Monitor data quality metrics and trends
• Drive remediation of **data issues and defects**
• Escalate material risks to VP\-level leadership

• *5\. Technical Partnership**

* Work closely with Data Custodians and Engineering to

+ Review data structures, transformations, and lineage

+ Support schema and contract changes

+ Translate business requirements into specifications

• Utilize data governance and catalog tools

• *6\. Cross\-Functional Collaboration**

* Collaborate with

+ Data Stewards

+ Engineering and platform teams

+ Risk, Compliance, and Legal

• Support enterprise initiatives by ensuring **data readiness and clarity**

• *Required Qualifications**

• *Experience**

• 6–10\+ years in Wealth Management or Financial Services
• Experience in:

+ Data product ownership, product management, or data governance

+ Working within large, complex enterprise environments

• *Wealth Management Expertise**

• Strong understanding of WM data domains and use cases
• Familiarity with WM business processes and regulatory considerations

• *Technical Knowledge**

* Working knowledge of

+ Data modeling, pipelines, and transformations

+ Data quality and reconciliation concepts

+ Metadata, lineage, and catalog tools

• Ability to effectively partner with technical teams

• *Key Attributes**

• Strong ownership mindset and attention to detail
• Execution\-focused and solution\-oriented
• Clear communicator across business and technical teams
• Comfortable working in structured governance environments

• *What Success Looks Like (12–18 Months)**

• Clear ownership and documentation for assigned data products
• Improved data quality metrics and reduced data defects
• Increased adoption and reuse of governed datasets
• Strong partnership with engineering and data stewards
• Effective contribution to enterprise data initiatives

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