Analytics Engineer

Are you someone who thrives on helping others succeed, enjoys making an impact, and takes pride in guiding customers to the right solutions for their projects? If you’re also naturally curious and eager to keep learning, consider starting or growing your career with us at The Home Depot. Position Overview 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.

Key Responsibilities

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 BigQuery, 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 BigQuery by applying dimensional modelling (curated marts, star schemas, conformed dimensions, hierarchies) and publishing governed, reusable datasets (e.g., standardised views and materialized views) to enable consistent metrics and self-service analytics. Partner with data engineers to ensure models are performant, scalable, and cost-efficient within BigQuery. 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 long-term maintainability. Act as a data modelling and analytics subject matter expert, providing guidance and recommendations across cross-divisional initiatives. Core Competencies Good judgment: You scope well, prioritize ruthlessly, and communicate trade-offs Analytics & Business Acumen: Strong ability to translate business and reporting needs into scalable data structures. Semantic Layer Design: Experience designing semantic models that power BI tools and self-service analytics. Collaboration: Proven ability to work closely with business partners, analytics leads, and engineering teams. Problem Solving: Analytical mindset with a focus on simplifying complexity and enabling insight. Data Quality & Governance: Strong focus on metric consistency, definitions, and trusted reporting. Technical Skills Advanced SQL with strong experience in Google BigQuery. Hands-on experience designing fact and dimension tables, star schemas, and analytical models. Experience working with inventory, supply chain, or retail domain data (strongly preferred). Familiarity with semantic layers and reporting tools (e.g., Looker, Tableau, Power BI, or equivalent). Understanding of performance optimisation and cost considerations in cloud data warehouses. Ability to partner effectively with data engineering teams on implementation and optimisation. Soft Skills Strong stakeholder communication skills, with the ability to explain complex data concepts in business-friendly language. Excellent documentation and requirement-gathering skills. Ability to work independently while collaborating across multiple teams. Detail-oriented with a strong sense of ownership and accountability. Education & Experience Bachelor’s degree in Computer Science, Engineering, Statistics, Mathematics, or a related quantitative field. 3-5 years of experience in data analytics, data modelling, or analytics engineering roles. Demonstrated experience designing enterprise-scale analytical and reporting data models. Experience working in retail, inventory management, or supply chain analytics environments is a strong asset. The pay range for this position is between $62,200.00 - $70,100.00