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Senior MDM Product Data Analyst (Bangalore, in-person)

Everpure · Posted Oct 16

Enterprise data storage and management platform for apps, analytics, and AI

Bangalore, IndiaFull-timeOnsiteSenior Level8+ years₹32.0L–₹48.0L yearly<25 applicants
Data StorageEnterprise Data CloudAI InfrastructureFlash StoragePublic Company
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About the role

Everpure (NYSE: P) is a data platform company spanning hyperscalers, AI labs, the AI hardware supply chain, and data platform providers. This role sits within product master data management, supporting the management, quality, governance, and transformation of product master data across enterprise systems. The analyst will work with Product, Business, Engineering, and Data Governance teams to analyze complex product data, define and validate data quality rules, support MDM onboarding and migration, and drive resolution of data issues using advanced SQL and AI/GenAI tools. This role is in-person in the Bangalore office.

What you will 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.

Skills used in this role

SQLSnowflakeReltioAtaccamaSalesforceZuoraNetSuiteCPQCRMSaaSGenerative AIAgileData ProfilingData QualityData GovernanceMaster Data ManagementProblem SolvingCommunicationStakeholder ManagementRoot Cause Analysis

What the employer is looking for

  • 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.
  • Hands-on experience with data quality frameworks, DQ rule development, data profiling, and issue remediation.
  • Experience performing source-to-target data analysis, reconciliation, mapping validation, and integration analysis.
  • Strong understanding of data governance concepts including data ownership, data standards, business rules, reference data, and allowed values.
  • Hands-on experience working with Reltio as an MDM platform.
  • Experience with CPQ, Product Catalog, Salesforce, Zuora, NetSuite, or CRM systems.
  • Experience working with hardware, software, SaaS, or subscription-based product data.
  • Experience with product configuration and product/component relationships.
  • Experience working with Snowflake.
  • Familiarity with automated data quality tools such as Ataccama.
  • Practical experience using AI/GenAI tools to improve data analysis, DQ rule development, documentation, and root-cause analysis.
  • Experience supporting UAT, data certification, defect triage, and cross-functional issue resolution.
  • Experience supporting large-scale MDM implementation, migration, or transformation programs.
  • Strong analytical, structured problem-solving, and attention-to-detail skills.
  • Excellent written and verbal communication skills, with the ability to present data findings, risks, and recommendations to both technical and business stakeholders.
  • Ability to work effectively across Product, Engineering, Data Governance, Business, and technical teams.
  • Experience analyzing complex product catalogs with high volumes of SKUs and attribute combinations.
  • Experience developing reusable data validation and reconciliation approaches for recurring data quality checks.
  • Experience identifying opportunities to automate manual data analysis, validation, and remediation activities using AI/GenAI or scripting.
  • Understanding of enterprise product data integrations and downstream consumption patterns.
  • Experience working in Agile delivery environments and participating in requirements grooming, backlog refinement, and data-focused delivery planning.

Benefits and support

  • Compensation and benefits are detailed in the job posting

About Everpure

Everpure logo

Everpure

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Everpure is an enterprise data storage and management company that unifies all data with an enterprise data cloud, making it easy to manage and get AI-ready fast. Formerly known as Pure Storage, the company delivers an industry-leading, ever-evolving storage and data management platform.

Industry
Data Storage
Company size
6000+ employees
Founded
2009
Location
Santa Clara, California, USA
Funding stage
Public Company

Funding

Public Company · $470M raised

Sutter Hill VenturesGreylock PartnersRedpoint Ventures
  • 2013-08-29Series E$150M
  • 2014-04-23Series F$225M

Leadership

CG
Charles Giancarlo

Chairman and CEO

RL

Rob Lee

Chief Technology Officer

DF

Dan FitzSimons

Chief Revenue Officer

Recent coverage

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