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Advisor Data Product Owner Wealth Management NYC, NY or Fort Mill, SC_hybrid only on w2

BURGEON IT SERVICES LLCNew York, NY🇺🇸United StatesPosted 25 Aug 2026

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
New York, NY, United States
Posted
18 hours ago
AgileJiraStakeholder Management

Job Description

Advisor Data Product Owner Wealth Management
Location: NYC, NY or Fort Mill, SC
Work Model: Hybrid | Travel Required
Duration: 12 months plus
Experience: 7 10+ years relevant experience

Please share your updated resume with me at pranayatburgeonitsdotcom

Role Overview
We are looking for a Techno-Functional Data Product Owner with strong Wealth Management and Advisor Data experience. The candidate will own Advisor Data requirements end-to-end, perform hands-on data analysis, manage Agile delivery, and work closely with business, compliance, compensation, operations, and technology teams.
Mandatory Skills
  • 7 10+ years of experience in Financial Services, preferably Wealth Management
  • Strong experience with Advisor / Producer Data
  • Hands-on Data Analysis, Data Mapping, Data Validation, and Data Quality
  • Knowledge of Advisor/Producer Profiles, Licensing & Registration, CRD/FINRA, Book of Business, Compensation/Grid, Branch/Organization Hierarchy
  • Experience with Source of Record (SOR), Golden Copy, MDM, Survivorship, Data Lineage, and Data Governance
  • Strong Techno-Functional Product Owner experience
  • Ability to create BRDs, data intake documents, requirements, NFRs, and implementation-ready Jira stories
  • Strong Agile, Jira, Backlog Management, Epics, Features, and User Stories experience
  • Working knowledge of APIs, Kafka/Event Streaming, Batch/File integrations, schemas, and data contracts
  • Strong stakeholder management across Wealth/Advisory, Compliance, Compensation, Operations, Risk, and Engineering
  • Ability to work independently and own deliverables end-to-end
Preferred
  • Experience with Advisor Workstations and CRM integrations
  • Experience using AI for requirements or data-quality analysis

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