- 2-4 years of experience as a Data Engineer on an enterprise team building large scale data integration using SQL and relational database
- 2+ years of experience as a Machine Learning Engineer building ML models using python and PySpark on cloud platforms
- Experience writing high quality code for reusable components.
- Experience with application observability, metrics driven development, test driven development, performance and operation.
- Experience with DevOps: AWS, CI/CD experience, Gitlab pipelines.
- MLOps experience a plus
- Snowflake and Databricks experience a plus
- Nice to have: Retail experience
- Nice to have: Experience in a large enterprise organization
Base City:
Vancouver - On-site
Salary:
$105k to $138k
Rating:
Self-taught:
Position Type:
Full-time
Position Keywords:
Experience:
2 Years Data Engineering
Other Experience:
About the Job:
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.
What they want you to do:
As a mid-level engineer on the team that owns the Demand Forecasting machine learning model development and operation for Product merchandising, allocation, and planning, you’ll…develop, maintain, and deliver desirable, viable, sustainable solutions to end users.work collaboratively with internal and external team members throughout development lifecycle to ensure system changes meet business needs.participate in designing, developing, testing, releasing and sustaining ML models.provide support and training to dependent teams who are utilizing demand forecast.support production issues as relate to application functionality and integration points.spend time documenting core parts of the system that enable seamless usage and increase adoption.