Professional - Data Product Analyst
lplfinancial · Hyderabad ·
- Experience
- 4+ years
- Company size
- 501-1000
Analyzes and shapes financial data products to ensure quality, governance, and usability for downstream teams at a wealth-management firm’s Hyderabad office.
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:
4+ 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:
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.
Core Competencies:
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 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.
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