Senior Data Engineer - 12 Month Contract

The Home Depot

2d•Onsite

About The Position

With a career at The Home Depot, you can be yourself and also be part of something bigger. Job Description Title: Senior Data Engineer- 12 Month Contract 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 retail disruptor. As we strive to deliver the best interconnected shopping experience for our customers, we invite you to join our rapidly growing and highly skilled Analytics team. As a Senior Data Engineer, you will design and build scalable data pipelines to organise, transform, and optimise billions of rows of data for analytics and decision-making. Leveraging Google BigQuery, Python, and retail domain expertise, you’ll deliver solutions that power insights, solve complex problems, and support both short- and long-term strategies. You’ll collaborate closely with business teams and data analysts to gather requirements and translate them into efficient transformations, building tables and views for dashboards to ensure clean, reliable, and high-performance data. Our team goes 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’re 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.

Requirements

Data engineering: designing and maintaining scalable data pipelines and ETL processes
Technical skills: proficiency in SQL, Python, Google BigQuery; familiarity with cloud platforms(GCP)
Business acumen: understanding of retail domain and ability to translate business requirements into data solutions
Collaboration: strong communication skills and ability to work effectively with cross-functional teams
Problem-solving: analytical mindset with focus on process optimisation and automation
Data quality: commitment to accuracy, governance, and performance across large datasets
Strong analytical and problem-solving abilities
Proficiency in data analysis and data engineering concepts
Excellent organisational and time management skills
Effective interpersonal and communication skills for cross-functional collaboration
Ability to manage projects and adapt to change in a fast-paced environment
5+ years of experience in data engineering or a related field.
Proficient in SQL, Python, and one or more big data technologies
Hands-on experience with Google Cloud Platform (GCP) services
Familiarity with data warehousing solutions like Redshift, BigQuery, or Snowflake
Experience building and maintaining ETL pipelines and working with large-scale datasets
Ability to design and implement stored procedures and optimise complex queries

Nice To Haves

Familiarity with clickstream data and its applications is beneficial.
Relevant technical certifications (e.g., AWS Certified Big Data – Specialty, Google Professional Data Engineer, Microsoft Certified: Azure Data Engineer Associate).
ITIL Foundation

Responsibilities

Design, develop, and maintain scalable data pipelines and ETL processes for structured and unstructured data.
Build and optimise tables and views for dashboards and self-service analytics.
Gather and translate business requirements into efficient data transformations.
Collaborate with business teams, data analysts, and technology partners to deliver reliable data solutions.
Ensure data quality, governance, and performance across large datasets.
Leverage Google BigQuery, Python, GBQ stored procedures, and retail domain expertise to develop robust solutions.
Automate processes and implement strategies to improve data quality across the customer experience.
Aggregate and prepare data for reporting, dashboards, and scorecards.
Provide data extraction and actionable insights to internal teams and respond to ad hoc analysis requests.
Drive continuous improvement by identifying process efficiencies and adopting modern data engineering tools and frameworks.
Effectively convey complex technical information to stakeholders with a business-oriented background, ensuring clarity and alignment with organisational objectives.