Senior 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.

Core responsibilities:

As a Senior 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. Focusing on data quality and system performance, and establish data engineering standards adopted across the domain, data modelling, Observability and alerting mechanism in cloud (Azure/AWS). Scalable Batch and Real time pipelines.

Select responsibilities include:

• Write exemplary data transformation code demonstrating data engineering best practices in critical systems as technical reference for teams.
• Designing, Building and maintaining scalable ETL data processing systems: creating and maintaining optimal data pipelines architecture.
• Assemble large, complex datasets meeting functional/non-functional requirements
• Identify, design and implement internal process improvements (Automating manual steps, optimizing delivery)
• Create data solutions for BI and data science 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
• Coordinating with cross teams and BAs for requirement analysis.

Skills must have:

• Deep knowledge of relational databases, cloud data warehouses, and selecting appropriate storage solutions for use cases; experience designing complex data architectures across multiple storage systems.
• Experience in Snowflake building data pipelines good to have AI Cortex, Snowflake notebook.
• Worked on orchestrator Airflow or Azure Data Factory
• 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 such as Apache Iceberg.
• Preferred experience in data stacks utilizing: Snowflake, AWS, Azure, GitLab, DBT.
• Build test and deploy software solutions in production.
• Data Quality standards and data validation.

Qualifications:

• Bachelor's degree in computer science, Data Science, Engineering, or related technical field, or equivalent experience; master's degree beneficial
• 8-12 years of experience building enterprise data solutions, data modelling, building scalable data pipelines (Batch Streaming), experience in Azure /AWS services.

Must haves Values:

• Communicate with honesty and kindness and create the space for others to do the same.
• Lead with courage, knowing the possibility of greatness is bigger than the fear of failure.
• Foster connection by putting people first and building trusting relationships.

Good to Have:

• Worked on Agentic solutions or any Use cases.
• Data governance.
• Demonstrated ability to establish data quality standards, quality and governance frameworks, and monitoring practices across a domain.
• Basic knowledge on Atlan, Horizon Cata Log.
• Basic understanding of AI/ML Model registry
• Mentoring new team members, demonstrate leadership qualities.