Professional_Data Product Analyst – Data Integrity Group

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:

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 Stewardship & Consumer Enablement

• Translate business use cases into structured data requirements

• Drive alignment across business, product, and technology teams

Data Migration & Modernization

• Supporting data migration, legacy decommissioning, or platform modernization programs

• Identify migration blockers and develop adoption strategies

Data Certification & Data Readiness

• Validate data quality, completeness, and reconciliation results

• Assess whether data is trusted and ready for enterprise consumption

DDA Intake & Prioritization

• Facilitate discovery sessions and gather requirements from stakeholders

• Prioritize competing initiatives based on business value, risk, complexity, and consumer impact

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.

• 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)

• Core Competencies:

• Experience supporting advisor data platforms, or shared enterprise data services.

• Background in financial services, wealth management, or other highly regulated data environments.

• Strong hands‑on experience with SQL (advanced querying, joins, performance awareness)

• Data Stewardship & Consumer Enablement

• Ability to understand how data is consumed across multiple business functions

• Experience translating business use cases into structured data requirements

• Strong understanding of downstream consumer impacts and adoption challenges.

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:

Data Migration & Modernization

• Experience working with Systems of Record (SORs), legacy systems, and transformation initiatives a plus

• Understanding of migration readiness, consumer onboarding, and adoption metrics

• Data Certification & Data Readiness

• Understanding of data certification processes and release readiness controls

• Experience evaluating downstream impacts of data changes

DDA Intake & Prioritization

• Strong analytical thinking and structured problem-solving skills

• Ability to convert vague requests into actionable backlog-ready work

Cross-Domain Data Analysis

• Advanced SQL and data exploration skills

• Ability to analyze data across multiple platforms and domains

• Ability to perform impact analysis across a distributed ecosystem

Consumer Access Layer & Data Consumption Patterns

• Understanding of APIs, shared data services, semantic layers, and enterprise data consumption patterns

• Experience helping organizations standardize data access

• Ability to identify opportunities for data reuse and eliminate duplicate consumption models.

• 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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