Senior Data Engineer - Retail Engineering (Snowflake)

Who we are

lululemon is an innovative performance apparel company for yoga, running, training, and other athletic pursuits. Setting the bar in technical fabrics and functional design, we create transformational products and experiences that support people in moving, growing, connecting, and being well. We owe our success to our innovative product, emphasis on stores, commitment to our people, and the incredible connections we make in every community we’re in.

As a company, we focus on creating positive change to build a healthier, thriving future. In particular that includes creating an equitable, inclusive and growth-focused environment for our people.

About this team

This team delivers Retail and OMNI data products that enable global store reporting and enterprise‑wide insights, supporting the business through trusted, scalable, and high‑quality data. Operating as a global group, the team owns the full data ecosystem—from ingestion and transformation to warehousing and analytics—covering semantic models, governance, technology operations, and new and emerging AI and data capabilities. Using tools such as Azure Data Factory, Snowflake’s Medallion architecture (Bronze, Silver, and Gold layers), and Power BI, the team partners closely across the enterprise to deliver curated, business‑ready data products.

Together, the team sets the technical direction for the data domain, making thoughtful decisions around orchestration, data modelling, transformation logic, semantic layer design, and performance optimization, while establishing strong standards for data quality, governance, and observability to ensure reliable insights at scale.

Core responsibilities

• 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 a 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, NoSQL databases, cloud data warehouses, 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
• Advanced Power BI expertise (5–7+ years)