$70,400 - 79,100 yearly
Position Purpose: Can you find exactly why a number moved, then explain it to a merchant and an analytics engineer in the same afternoon? That's everyday work for our Decision Analytics team in Finance. We build the data products, scorecards, and AI tools that finance and merchandising leaders across The Home Depot Canada rely on. And we do it in a company built on strong relationships and respect for all people.
In this role, you will own the analytics behind key finance and merchandising decisions, from markdowns and clearance to store performance. You will run complex reporting workflows, advise partners on test design, and turn results into recommendations they act on. We're hiring at the Insights Analyst or Senior Insights Analyst level, depending on experience; senior hires work more independently and lead product ownership. Apply today and help shape the next chapter.
Key Responsibilities and Tasks Key Responsibilities Tasks
Finance and Merchandising Analytics & Decision Support
· Business Rules Ownership: Own the business rules and analytics behind finance and merchandising decisions such as markdowns and clearance.
· Complex Reporting Workflows: Run and maintain the technically complex reporting workflows partners act on, keeping them accurate, documented, and on time.
· Test & Learn Guidance: Advise finance and merchandising partners on how to design their tests, including control selection, duration, and success metrics, so the test and learn methodology answers their objective.
· Performance Measurement & Insights: Build Tableau dashboards and scorecards that connect business decisions to store and financial results, including cost-benefit analysis. Turn the results into recommendations partners can act on.
Data Quality, Automation & AI Innovation
· Root-Cause Troubleshooting: Trace a wrong number back to its cause, fix what's in scope, and hand analytics engineering a clear diagnosis rather than a symptom.
· Workflow Modernization: Move legacy workflows, including Alteryx, to SQL- and Python-based processes in partnership with analytics engineering.
· AI Integration: Use enterprise AI tools such as Gemini, Copilot, and NotebookLM to automate workflows and build AI agents or assistants for partner teams.
Cross-Functional Collaboration & Business Impact
· Product Ownership: Act as the business-side owner for analytics tools, writing requirements and acceptance criteria, validating what analytics engineering builds, and driving adoption.
· Communication: Explain technical issues and their impact in business language that finance and merchandising business partners can understand. Learn how partners use your tools so you can improve them.
Nature and Scope
Minimum Requirements
Education & Experience
· Education: Bachelor's degree in Business Analytics, Finance, Economics, Statistics, Computer Science, Engineering, or a related quantitative field.
· Experience: 2–4 years for Insights Analyst or 4–6 years for Senior Insights Analyst, in analytics, business intelligence, or a related field. Retail experience is highly beneficial.
· Certifications (Bonus): Relevant certifications are an asset, such as Tableau or Alteryx certifications.
Technical Proficiency
Must-haves apply at both levels; we weigh assets by level and expect senior candidates to bring most of them. Each item is tested, including in a live exercise and a dashboard walkthrough.
· SQL (Must-Have): Writing complex SQL in a cloud warehouse (BigQuery preferred) and finding why a query returns the wrong number.
· Tableau (Must-Have): Building and maintaining dashboards that business users rely on.
· Troubleshooting (Must-Have): Tracing a wrong number to its source and telling data, logic, and business-rule issues apart.
· AI Fluency (Must-Have): Using AI tools daily, checking their output, building with them, and knowing what data stays out.
· Microsoft Office (Must-Have): Using advanced Excel, such as Power Query, XLOOKUP, dynamic array formulas, and scenario models, and building PowerPoint presentations for business partners.
· Python (Asset): Transforming data and automating workflows; reading and explaining others' code.
· Legacy Workflows (Asset): Running, documenting, and migrating workflows built by others; Alteryx experience preferred.
· Requirements (Asset): Writing requirements and acceptance criteria analytics engineers can build from.
· Test Design (Asset): Understanding test-versus-control measurement well enough to advise partners.
Skills & Competencies
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