Data Analyst, Master Data Management, Supply Chain

Pure Storage•Santa Clara, CA

1d•Onsite

About The Position

At Everpure, we are building the future of supply chain operations, and trusted master data is the foundation of our success. As our Data Analyst for Supply Chain Master Data Management (MDM), you will champion data integrity by translating complex data sets into actionable health metrics and operational reporting. You will partner closely with global Supply Chain, Order Management, IT, and external Contract Manufacturers to diagnose systemic data gaps and drive process improvements. Your analytical insights will directly reduce operational complexity, ensuring our data scales seamlessly with our growth.

Requirements

Proven technical proficiency in querying and modeling data using SQL, alongside hands-on experience building operational reporting inside modern BI tools.
Strong structural understanding of enterprise architecture, specifically how master data flows through ERP systems (SAP) and impacts planning, sourcing, logistics, and order management.
Demonstrated ability to dissect complex data anomalies, perform root-cause analysis, and connect data health directly to downstream business outcomes.
Exceptional ability to translate technical data findings, anomalies, and requirements into clear, actionable insights for both technical IT teams and non-technical business stakeholders.

Responsibilities

Engine Data Quality Monitoring: Build, maintain, and scale dashboards and KPI reporting to measure the health, compliance, and lifecycle controls of core supply chain domains (item, product, bill of materials, sourcing, and logistics).
Drive Root Cause Remediation: Conduct deep-dive root cause analysis on master data anomalies and integration failures across enterprise systems (SAP), partnering with data stewards and technical teams to implement long-term structural fixes.
Optimize External Ecosystems: Liaise with Contract Manufacturers to track and govern critical shared data elements, eliminating friction points and ensuring smooth cross-organizational operations.
Advance Data Governance Standards: Design and track continuous improvement data quality rules, translating complex analytical findings into actionable recommendations for future governance workflows and exception reporting.