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.

Do you have a learning mindset and a passion for data-driven decision-making? Do you thrive in a dynamic environment where you can leverage technology to uncover efficiencies?  Would working in a highly collaborative and relationship driven company that values respect for everyone excite you? Look no further than Home Depot Canada.

We go beyond routine reporting; we engage in creative, hands-on work within an empowering environment where every decision is backed by high-quality data. If you are an innovator passionate about data engineering and reimagining the future of retail, apply today and help shape the next chapter at The Home Depot Canada.

As a Data Engineer, you will design and build scalable data pipelines to organize, transform, and optimize billions of rows of data for analytics and decision-making. Leveraging Google BigQuery (GBQ), Python, and retail domain expertise, you will deliver solutions that power insights, solve complex problems, and support short- and long-term strategies. You will collaborate closely with business teams and data analysts to translate requirements into efficient transformations, ensuring clean, reliable, and high-performance data across the organization.

At the Home Depot the Financial Analyst is responsible for:

Data Architecture & Pipeline Engineering

• Pipeline Development: Design, develop, and maintain scalable data pipelines and ETL processes for both structured and unstructured data.
• Data Transformation: Build and optimize tables and views for dashboards and self-service analytics, gathering and translating business requirements into efficient data transformations.
• Technical Execution: Leverage Google BigQuery, Python, and GBQ stored procedures to develop robust solutions and optimize complex queries.

Data Optimization & AI Innovation

• AI Integration: Leverage and integrate advanced AI tools—such as Gemini, GitHub Copilot, NotebookLM, Vertex AI etc.—to enhance data solutions, automate complex workflows, and build next-generation analytics capabilities.
• Governance & Accuracy: Ensure data quality, governance, and high performance across large-scale datasets.
• Automation: Automate processes and implement strategies to continuously improve data quality across the customer experience.
• Continuous Improvement: Drive operational efficiency by identifying process bottlenecks and adopting modern data engineering tools and frameworks.

Cross-Functional Collaboration & Business Impact

• Strategic Partnering: Collaborate with business teams, data analysts, and technology partners to deliver reliable, actionable data solutions.
• Insight Delivery: Aggregate and prepare data for reporting, dashboards, and scorecards. Provide data extraction support and respond to ad hoc analysis requests.
• Communication: Effectively convey complex technical information and data constraints to stakeholders with a business-oriented background, ensuring alignment with organizational objectives.

What you will need to be successful in this role:

Education & Experience

• Education: Bachelor’s degree in Computer Science, Engineering, Statistics, Applied Mathematics, or a related technical/quantitative field.
• Experience: 3-5 years of experience in data engineering or a related field. A strong understanding of the retail domain are highly beneficial.
• Certifications (Bonus): Relevant technical certifications are a strong asset (e.g., Google Professional Data Engineer, AWS Certified Big Data – Specialty, Microsoft Certified: Azure Data Engineer Associate).

Technical Proficiency

• Core Languages: Deep proficiency in SQL and Python.
• Cloud & Big Data: Hands-on experience with Google Cloud Platform (GCP) services and one or more big data technologies.
• AI & Machine Learning Tooling: Familiarity with or strong interest in using AI platforms and coding assistants (such as Vertex AI, Gemini, NotebookLM, GitHub Copilot etc.) to accelerate development and build smarter data solutions.
• Data Warehousing: Strong familiarity with modern data warehousing solutions such as BigQuery, Snowflake, or Redshift.
• Engineering Concepts: Proven experience building and maintaining complex ETL pipelines, designing stored procedures, and working with large-scale datasets.

Skills & Competencies

• Innovation & AI Mindset: A forward-thinking approach with a passion for adopting Generative AI and automation to solve complex problems and reduce manual engineering tasks.
• Problem Solving: Strong analytical mindset with a core focus on process optimization, automation, and continuous improvement.
• Communication: Excellent interpersonal skills with the ability to bridge the gap between highly technical engineering work and business strategy.
• Agility: Excellent organizational and time management skills, with the ability to manage multiple projects and adapt to change in a fast-paced environment.