Analytics Engineer

Analytics Engineer at The Home Depot in Toronto, ON M3C 4H9 Job Description

Pay Range: $65,000 - $95,000

At The Home Depot Canada, we want you to feel valued and supported. The pay range you see represents base salary only. In addition, your total rewards may include: semi-annual bonuses tied to business performance;
Deferred Profit-Sharing Program to assist with retirement savings; comprehensive paid benefits; a 15% discount on Home Depot stock purchases; and merit-based salary increases. We are committed to recognizing your efforts and supporting your growth with us.

Our entrepreneurial spirit and innovative mindset, combined with the resources and support of The Home Depot Canada—the world’s leading home improvement retailer—offer a unique opportunity to join a transformative, data-driven organisation.

You’ll be the data layer for the entire company — a strategic partner to Finance, Marketing, Sales, Product, Supply Chain and Data Science — designing the clean, trusted datasets that power our traditional BI and next generation AI tooling and ensuring every team can make decisions from a single source of truth.

This role is highly focused on data modelling, analytical thinking, and business-facing design, rather than pure pipeline engineering. You will work at the intersection of business requirements, data domain knowledge, and analytics consumption, ensuring that data structures are intuitive, performant, and aligned to enterprise standards.

The Analytics Engineer is critical to ensuring that inventory data is modelled once and used consistently across the organisation, enabling trusted reporting, faster insights, and scalable analytics. The successful candidate will play a key role in shaping how inventory data is structured, governed, and consumed across business and analytics teams.

• Build and own the canonical data models in Big Query that serve as Home Depot Canada’s company-wide source of truth — clean, queryable, and AI-ready
• Partner with business stakeholders, analytics leads, and data engineering teams to gather and clarify reporting and analytical requirements related to data, translating business questions into well-defined analytics and data modelling requirements.
• Structure datasets so vendor AI tools perform optimally out of the box, with consistent schemas, rich semantics, and well-indexed access patterns
• Design, document, and recommend logical and physical data models for data stored in Google Big Query, optimised for analytics and reporting use cases.
• Design data models with clear entity relationships, metadata, and business definitions.
• Anticipate future AI-driven needs by modelling data with high data quality, interpretability, historical tracking, and feature reusability in mind.
• Define and implement dimensional data models (facts, dimensions, conformed dimensions, hierarchies) that support reporting, trends, and performance analysis.
• Partner with data architects to inform the design of a semantic/reporting layer in Big Query by applying dimensional modelling (curated marts, star schemas, conformed dimensions, hierarchies) and publishing governed, reusable datasets (e.g., standardised views and materialised views) to enable consistent metrics and self‑service analytics.
• Partner with data engineers to ensure models are performant, scalable, and cost‑efficient within Big Query.
• Establish and promote best practices for data modelling, metric definition, and semantic consistency across analytics teams including eliminating shadow tables and one‑off datasets by proactively serving team data needs at the platform level, freeing data science to move faster
• Support analytics and reporting teams by enabling clear interpretation of reporting metrics and data structures.
• Document data models, assumptions, definitions, and lineage to support governance, onboarding, and…