Data Engineer

Who we are:

Founded in 1998 at Vancouver, lululemon is a performance and lifestyle product company that create transformational products and experiences that build meaningful connections, unlocking greater possibility and wellbeing for all. We are driven by our brand purpose to elevate human potential by making individuals feel their best which helps us design our products with high filter and high style. We use a unique product creation methodology called Science of Feel in all our products to offer convenient, comfortable, and long-lasting experience. We owe our success to our innovative products, commitment to our people, and the incredible connections we make in every community we're in.

About this team:

The Enterprise Data & AI organization is a strategic and operational driver of growth at lululemon. Within Enterprise Data & AI, our team partners closely with the CFO organization to build the trusted data foundations, analytics solutions, and reporting capabilities that power financial decision-making across the company. We own the end-to-end journey from developing scalable data pipelines and enterprise data products to enabling the reporting and analytics used by financial analysts, finance leaders, and senior leadership teams. Our work transforms complex financial and operational data into trusted, governed, and accessible insights that support everything from day-to-day analysis to executive and board-level reporting. By combining data engineering excellence with business impact, we help drive smarter decisions, greater transparency, and long-term growth across the organization.

Core responsibilities:

The Enterprise Data & AI team is at the center of how lululemon scales as a data-driven organization. As a Data Engineer, you will lead the design and implementation of complex data systems and pipelines spanning multiple data sources and destinations, establishing technical direction for the data domain including data quality standards, pipeline patterns, and governance practices while ensuring solutions meet scalability and reliability requirements. You will make implementation decisions for data domain including data modeling approaches, transformation logic, and optimization strategies, conduct high-impact code reviews focusing on data quality and system performance, and establish data engineering standards adopted across the domain. You will mentor data engineers at multiple levels providing guidance on advanced data concepts and career development, partner with analytics and business stakeholders on data strategy and roadmap planning, and lead resolution of critical data quality issues and pipeline failures.

• Lead implementation of complex data systems and pipelines spanning multiple data sources and destinations within established data architecture frameworks
• Write exemplary data transformation code demonstrating data engineering best practices in critical systems as technical reference for teams
• Establish and enforce data quality standards, pipeline patterns, and data engineering best practices across multiple teams in data domain
• Conduct high-impact code reviews focusing on data quality, performance, and long-term maintainability of data systems
• Lead incident response for major data pipeline failures including coordination and post-mortem facilitation

Qualifications:

• Bachelor's degree in Computer Science, Data Science, Engineering, or related technical field, or equivalent experience; Master's degree beneficial
• 6-10 years of experience building enterprise data solutions and leading data infrastructure initiatives across engineering teams, or equivalent
• Proven experience designing data system architectures for a domain balancing data requirements and constraints; track record of driving data pipeline design decisions across multiple teams
• Deep knowledge of relational databases (MySQL), NoSQL databases, cloud data warehouses(Amazon Redshift / Azure Synapse), and selecting appropriate storage solutions for use cases; experience designing complex data architectures across multiple storage systems
• Demonstrated ability to establish data quality standards, quality and governance frameworks, and monitoring practices across a domain
• Expert-level experience designing and optimizing data solutions using Snowflake, including data modeling, performance tuning, workload management, and scalable data product development
• Strong experience implementing modern data engineering patterns, including workflow orchestration, metadata-driven pipelines, CI/CD, and Lakehouse architectures leveraging open table formats.
• Experience in data stacks utilizing: Snowflake, AWS / Azure, GitLab, DBT (Good to have)