MDM Product Data Analyst

Everpure (NYSE: P) has evolved from storage pioneer to data platform, closing fiscal 2026 with $3.7 billion in revenue, its first billion-dollar quarter, and accelerating growth into FY27. Our strategic agenda spans the companies defining the next era of technology - hyperscalers, AI labs, the AI hardware supply chain, data platform providers, and the broader AI ecosystem.

This type of work—work that changes the world—is what the tech industry was founded on. So, if you're ready to seize the endless opportunities and leave your mark, come join us.

THE ROLE

We are seeking an experienced MDM Product Data Analyst to support the management, quality,
governance, and transformation of product master data across enterprise systems. The role will work
closely with Product, Business, Engineering, Data Governance, and technical teams to analyze complex
product data, define and validate data quality rules, support MDM onboarding and migration, and drive
resolution of data issues. The ideal candidate combines strong product data and MDM expertise with
advanced SQL, analytical problem-solving, and practical use of AI/GenAI to improve data analysis and
delivery.

WHAT YOU'LL DO
• Analyze, manage, and reconcile product master data across multiple source systems, MDM platforms,
and downstream applications.
• Leverage AI/GenAI tools to accelerate data profiling, analysis, data quality rule development,
documentation, issue investigation, and root-cause analysis.
• Translate business and product requirements into clear data requirements, attribute definitions,
validation rules, and analytical specifications.
• Perform detailed data profiling to identify quality issues, gaps, inconsistencies, anomalies, duplicates,
and data integrity risks across product attributes and relationships.
• Define, document, implement, and validate data quality rules covering completeness, accuracy,
validity, consistency, uniqueness, and business-rule compliance.
• Develop source-to-target analysis and reconciliation approaches to validate data movement,
mappings, transformations, and synchronization across systems.
• Work with Product and Business stakeholders to understand product structures, SKUs,
product/component relationships, configurations, classifications, and product lifecycle requirements.
• Analyze and resolve complex product data issues, including incorrect attribute values, missing data,
duplicate records, mapping issues, hierarchy discrepancies, and configuration gaps.
• Support MDM data onboarding, enrichment, cleansing, migration, and transformation activities,
including data preparation and validation. • Develop data validation logic and partner with technical teams on implementation, testing, and automation of DQ rules. • Participate in UAT, data certification, defect triage, root-cause analysis, and issue resolution; ensure defects are clearly documented and tracked through closure. • Create data correction files and coordinate with technical teams to implement and validate required fixes. • Support development and maintenance of product data dictionaries, attribute definitions, business rules, reference data, and data governance documentation. • Partner with Data Governance teams to define data ownership, standards, allowed values, reference data, and validation requirements. • Monitor and report data quality metrics, trends, defects, and remediation progress, providing clear status updates and recommendations to stakeholders. • Identify recurring data quality issues and recommend process, data model, integration, or system improvements to prevent recurrence. • Contribute to large-scale MDM implementation, migration, and transformation programs and support continuous improvement of product data processes. Required Skills & Experience • 8+ years of relevant experience in Data Analytics, Master Data Management, Product Data Management, and/or Data Quality. • Strong hands-on experience working with Product Master Data and Product Catalog data. • Strong understanding of SKU structures, product hierarchies, attributes, product/component relationships, configurations, lifecycle, and classification. • Strong SQL skills for data extraction, profiling, reconciliation, validation, and investigative analysis.