Senior Data Product Owner – Wealth Management Participant Data Domain  at SBT Global, Inc.

Hyderabad, Telangana, India -

Full Time

Start Date

Immediate

Expiry Date

17 Oct, 26

Salary

0.0

Posted On

19 Jul, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

SQL, Data Product Ownership, Product Analytics, Data Modeling, Data Governance, Agile/Scrum, Impact Analysis, Data Lineage, Jira, Data Quality Validation, Semantic Consistency, Event-Driven Architectures, Stakeholder Management, Product Discovery, Financial Services Knowledge, AI-Enabled Data Analysis

Industry

Financial Services

Description

Where Ambition Meets Innovation At LPL’s Global Capability Center, you'll find a collaborative culture where your voice matters, integrity guides every decision, and technology fuels progress. Your skills, talents, and ideas will redefine what's possible. LPL's success reflects its exceptional employees, who together pursue one noble purpose: empowering financial advisors to deliver personalized advice for all who need it. We’re proud to be expanding and reaching new heights in Hyderabad. Join us as we create something extraordinary together. Job Overview: LPL Financial is seeking a Data Product Analyst to support our Strategic Data Platforms, with a strong focus on how data is produced, shaped, governed, and delivered to downstream consumers across the firm. This role sits at the intersection of Product Management, Data Product Ownership, and Advanced Analysis, and is designed for a senior individual contributor who can bring rigor and clarity to complex, data‑centric platform initiatives. The Data Product Analyst focuses on data as a product—helping define data contracts, validate quality, assess downstream impact, and support data product and platform decisions. The ideal candidate is comfortable working across upstream systems of record, platform services, and downstream consumers, and excels at turning complex data flows into actionable insights and decisions. Responsibilities: Product Discovery & Capability Support Support product discovery by helping translate business and platform needs into clear data‑centric problem statements. Contribute to roadmap planning by evaluating value, risk, and complexity across data initiatives. Help define outcomes and success measures for data platform capabilities (e.g., usability, reliability, adoption, quality). Advanced Data Analysis & Problem Solving Perform hands‑on exploratory and diagnostic analysis across distributed data sources and platforms. Investigate data quality issues, lineage gaps, and semantic inconsistencies that affect downstream consumers. Conduct impact analysis for schema changes, new feeds, platform migrations, or system decommissions. Identify systemic issues in data flow, transformation, or consumption and recommend durable solutions. Data Product Ownership (Supporting / Platform‑Level) Support Data Product Owners by helping define: Data contracts, schemas, and semantic definitions Data quality rules, validation logic, and controls Usage expectations and downstream dependencies Maintain traceability between business intent, data products, and technical implementation. Partner with engineering, architecture, and data platform teams to support scalable, reusable distribution patterns. Agile Execution & Delivery Contribute to Jira artifacts (features, stories, acceptance criteria) with strong data and platform context. Participate in backlog refinement, sprint planning, and demos to ensure work reflects validated platform and consumer needs. Support prioritization decisions by providing evidence‑based impact and dependency analysis. Stakeholder Communication & Influence Act as an analytical partner to Product Managers, Platform Owners, Engineering, and downstream consumer teams. Communicate complex data flow and platform concepts in clear, decision‑ready narratives. Help align stakeholders across domains by grounding conversations in data realities and trade‑offs. What are we looking for? We’re looking for strong collaborators who deliver exceptional client experiences and thrive in fast-paced, team-oriented environments. Our ideal candidates pursue greatness, act with integrity, and are driven to help our clients succeed. We value those who embrace creativity, continuous improvement, and contribute to a culture where we win together and create and share joy in our work. Requirements: 5+ years of experience in Product Analytics, Data Analysis, Data Product Ownership, or Platform‑oriented Product roles. Strong hands‑on experience with SQL (advanced querying, joins, performance awareness) Experience working with data platforms, distributed data systems, or integration layers. Experience adopting emerging AI‑enabled capabilities to improve the speed, depth, and effectiveness of data analysis Experience operating in Agile/Scrum environments with cross‑functional product and engineering teams. Proven ability to work across ambiguity and translate complex data ecosystems into actionable insights. Solid understanding of: Data modeling and semantic consistency Data quality, lineage, and governance concepts Pipelines, and event‑driven architectures (conceptual and analytical understanding) Preferences: Data platform decisions are made with clear understanding of downstream impact and value. Platform teams have strong analytical support when defining and evolving shared data capabilities. Data quality and semantic issues are identified early, before they impact consumers. Core Competencies: Experience supporting advisor data platforms, or shared enterprise data services. Familiarity with downstream consumer needs (applications, analytics platforms, partners, regulatory consumers). Experience performing impact analysis for data changes across multiple consuming systems. Background in financial services, wealth management, or other highly regulated data environments. LPL Global Business Services, LLP - PRIVACY POLICY

Responsibilities

The role focuses on managing data as a product by defining data contracts, schemas, and quality rules for strategic data platforms. It involves performing advanced diagnostic analysis to resolve data flow issues and collaborating with engineering teams to ensure scalable distribution patterns.