Data Engineer [T500-30296]

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.

Data Engineer:

Core Responsibilities:

• Partner with project management to deliver status and performance updates
• Lead development of observability initiatives that internal users can understand, and monitor
• Experience in creating ETL pipelines and pipeline orchestration using Microsoft Azure Data Factory
• Understanding of Snowflake Database functionality and working knowledge of external and internal stages, streams, table types and alerts, procedures in Snowflake
• Engages in ongoing quality and performance improvement to ensure reliability and data security
• Work closely with data architects to implement data design best practices, ensuring that our data reporting systems align with industry standards and promote scalable and efficient data architecture.
• Conduct research to support product development decisions, aiding in the selection of tools that optimize our reporting capabilities
• Provide an escalation point and participate in on-call support rotations
• Actively monitor key metrics, reports on trends, and make improvement recommendations
• Actively participate in knowledge-sharing sessions within the team to foster a collaborative learning environment
• Works in an agile environment, contributing to story sizing and reviews user stories to ensure a quality user experience, well-defined acceptance criteria and thorough test coverage.

Qualifications:

• Bachelor’s degree in computer science/engineering or similar
• 8+ years of data engineering experience with focus on designing and implementing scalable data transformations and pipelines
• Proficiency with Cloud Platforms like Azure(preferred), AWS, and/or Google cloud, including knowledge of core services like Azure Data factory Log Analytics, Logic Apps & KQL.
• Proficiency in scripting languages like SQL, Knowledge of python and hands on experience in Databricks would be good to have
• Familiarity with project management and collaboration tools like JIRA and/or Azure Devops for tracking tasks, managing workflows and facilitating communications within the team