Data Design Lead

Staples Canada•Richmond Hill, ON

58d•Hybrid

About The Position

We are seeking a hands-on Data Design Lead with strong technical skills in data governance implementation, data modelling, SQL/NoSQL databases, and cloud data platforms. This role partners closely with Data Engineers, BI teams, and business stakeholders to ensure high-quality, well-governed data is available for reporting, analytics, and operational use across Staples Canada’s retail and e-commerce systems. The successful candidate will design robust data models, implement governance processes, define and enforce data standards, improve data reliability, and support GCP-based analytical solutions.

Requirements

Bachelor’s degree in Information Technology, Computer Science, Data Management, or related field.
7 + years in data management, data modelling, data governance, or data engineering support roles.
Experience working in a retail or e-commerce environment is strongly preferred.
Strong SQL expertise across RDBMS (MS SQL Server, BigQuery) and exposure to NoSQL environments.
Hands-on experience with dimensional modelling, analytical modelling, and retail data structures.
Working knowledge of GCP data products (BigQuery, Cloud Storage, Dataflow, Pub/Sub, Dataplex, etc.).
Understanding of ETL/ELT concepts and pipeline design.
Familiarity with BI tools: Power BI, Looker.
Experience with data governance or data quality tools and principles.
Strong understanding of retail and e-commerce data flows and KPIs.

Responsibilities

Design and maintain conceptual, logical, and physical data models to support retail analytic workloads.
Partner with analytics teams to expose clean, accurate, governed datasets in BigQuery, Power BI, and Looker.
Work daily with data engineers to ensure data pipelines meet governance, quality, and modelling standards.
Contribute to the design of ETL/ELT pipelines for onboarding new datasets.
Support architecture/design reviews for new data integrations and Power BI semantic models as required.
Continuously improve modelling practices, governance processes, and data reliability.