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Workday Master Data & Automation Analyst
Loblaw Companies Limited
Brampton, CAN
Full-time
Entry
Onsite
$68,000–$93,500 per year
Discovered 1 weeks ago
Workday foundation dataWorkday reportingHRIS and HR operationsData governanceData quality managementData loads and Enterprise Interface Builder
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Workday foundation dataWorkday reportingHRIS and HR operations
Role Overview
The Workday Master Data & Automation Analyst supports Workday foundation data, reporting, data quality monitoring, data loads, documentation, issue tracking, automation, and practical AI enablement.
The role is suited to a Workday or HRIS analyst who uses trusted data to support AI enablement, decision-making, and automation across Workday and HR operations.
Key Skills for This Role
Workday foundation dataWorkday reportingHRIS and HR operationsData governanceData quality managementData loads and Enterprise Interface Builder
Full Job Posting
Company Overview
Loblaw Companies Limited describes a collaborative workplace supporting communities across Canada and offering opportunities across career stages.
The company emphasizes collaboration, commitment, training, flexibility, sustainability, social impact, and inclusion.
Role Overview
The Workday Master Data & Automation Analyst supports Workday foundation data, reporting, data quality monitoring, data loads, documentation, issue tracking, automation, and practical AI enablement.
The role is suited to a Workday or HRIS analyst who uses trusted data to support AI enablement, decision-making, and automation across Workday and HR operations.
What You’ll Do
- Maintain Workday master data management and governance, including data dictionaries, standards, ownership, quality rules, and issue tracking.
- Build and maintain Workday reports and data-quality dashboards to monitor data and remediation progress.
- Analyze Workday data with Excel and approved tools to identify quality risks.
- Support Workday data loads through preparation, validation, testing, and post-load review.
- Document AI and automation use cases, data needs, process steps, risks, benefits, and responsible AI considerations.
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