Senior Data Quality Analyst, Master Data, Procurement Services

As the Senior Data Quality Analyst for Master Data within PG&E’s Procurement Services organization, you will drive enterprise-level data quality strategies and operational excellence across procurement master data domains (e.g., vendor, material, contract, pricing). You will ensure that data assets are accurate, complete, and fit for purpose by defining and executing data quality rules, conducting validations and profiling, implementing monitoring and reporting frameworks, and driving corrective actions in collaboration with cross-functional stakeholders. 

This role expands upon traditional master data stewardship to include robust data quality ownership, focusing on proactive governance, measurable health metrics, remediation frameworks, and continuous improvement of data quality across PG&E’s procurement systems. 

Key Responsibilities 

Data Quality Definition & Implementation 

• Define, document, and maintain data quality rules, validation checks, and acceptance criteria for procurement of master data elements (e.g., Service Codes, Vendor IDs, contract terms, material attributes). 

• Develop and implement scalable data quality frameworks that integrate both preventative and detective controls. 

• Collaborate with business SMEs, data stewards, and IT partners to set thresholds, standards, and KPIs for data quality measurement. 
    

Data Quality Monitoring & Reporting 

• Design and operationalize data quality dashboards and reports to monitor data health, trends, and compliance against standards. 

• Collect, analyze, and present data quality metrics and insights to leadership and stakeholders, identify patterns, risks, and opportunities for improvement. 

• Track and escalate data issues using structured issue tracking and remediation workflows. 

Data Profiling & Root Cause Analysis 

• Perform regular data profiling, completeness checks, and anomaly detection to validate data quality against business rules and governance standards. 

• Conduct root cause analysis to identify systemic issues and propose corrective actions with process owners and technology teams. 
    

Governance & Standards 

• Support and enforce data governance policies and master data standards across procurement systems, ensuring alignment to enterprise frameworks. 

• Participate in governance forums and working groups to formalize stewardship roles, responsibilities, and data quality accountability. 

Continuous Improvement 

• Lead process improvement initiatives to eliminate data quality gaps and reduce manual intervention. 

• Identify opportunities for automation of quality checks and validation logic in collaboration with engineering and platform teams. 

Stakeholder Engagement 

• Build strong relationships with procurement, supply chain, finance, and IT teams to drive data quality initiatives, change management, and shared ownership. 

• Provide coaching, best-practice guidance, and advocacy on data quality principles across the organization. 

• Bachelor's degree in Computer Science, MIS, Business Analytics, or related field (or equivalent experience). 

• 5+ years of experience in data quality, master data management (MDM), or data governance roles supporting enterprise data functions. 

• Strong understanding of data quality dimensions such as accuracy, completeness, consistency, validity, and timeliness. 

• Demonstrated experience writing and implementing data quality rules, validations, and thresholds. 

• Experience with data profiling, analytics, and quality reporting tools. 

Desired 

• Knowledge of MDM platforms, data governance/quality tools, and automated monitoring frameworks (e.g., Collibra, Informatica, Talend, Great Expectations). 

• Familiarity with SQL and scripting languages to support data profiling and quality automation. 

• Experience working with ERP (e.g., SAP) and procurement systems (e.g., Ariba). 

• Proven success in cross-functional collaboration and influence within complex enterprise environments. 

Measures of Success 

• Effective definition and operationalization of data quality rules and metrics covering key procurement master data domains. 

• Increased data quality scores over time (measured via dashboards and KPIs). 

• Strong stakeholder engagement resulting in timely remediation of data issues and decreased rework. 

• Continuous improvements to data governance processes and automation yield enhanced data trust and reduced manual effort.