Senior Engineer I DevOps
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 :
lululemon Engineering is dedicated to building secure, reliable, and performant products for our guests and partners. We embrace the philosophies of Agile, DevOps, and SRE to accelerate our development process and provide the most enjoyable, inclusive, and supportive work environment possible. We believe our journey is more fun when it is collaborative, as we focus on the future instead of the past. As an Engineer, you will work as part of a global team supported by our business and architecture partners to help us collaboratively develop and deliver industry leading technology solutions that drive lululemons business goals.
Core Responsibilities :
As a Senior Engineer I, you will bring a high level of technical knowledge as well as strong mentoring abilities. You will be counted on as a leader in your technology space as you contribute to all areas of development and operations (pre-production to production). You will work closely with a Technology Manager, using your experience and knowledge to guide a team of Engineers though their day-to-day process, and provide a central escalation point for production concerns. You will be part of an Agile production release team and may perform on-call support functions as needed. As a Senior Engineer I, you would be a primary caretaker of production systems and would maintain a deep understanding of how delivered products are functioning.
- Are a main contributor in Agile ceremonies
- Provide mentorship and facilitate engineering training for a team of Engineers
- Perform and delegate engineering assignments to ensure production readiness is maintained
- Conduct research to aid in product troubleshooting and optimization efforts
- Conduct research to guide product development and tools selection
- Provide an escalation point and participate in on-call support rotations
- Actively monitor key metrics and report on trends
- Participate in our Engineering Community of Practice
- Contribute to engineering automation, management or development of production level systems
- Contribute to project engineering design and standards verification
- Perform reliability monitoring and support as needed to ensure products meet guest expectations
Qualifications :
Bachelors or advanced degree in Computer Science, Software Engineering, or related field.10+ years of experience in software development.At least 5 years of hands-on experience in DevOps.Strong knowledge of DevOps tools and practices for build and deployment automation.Excellent verbal and written communication skills.Strong expertise with build tools such as Ant or MavenCI / CD tools, including Jenkins or GitHub CI / CDCloud DevOps, particularly with AWS services (Code Build, CodeDeploy, CodePipeline) or Azure DevOpsContainerization technologies, including Docker and Docker Registry.Infrastructure as Code, using tools like Terraform and Helm charts.Deploying and maintaining Kubernetes clusters.Basic Proficiency with Streaming and big data technologies , such as Spark, Kafka, and Hadoop.Artifactory tools, like JFrog.Monitoring and reporting tools, including Prometheus, Grafana, and PagerDuty.Databases such as MySQL or PostgreSQL, especially for monitoring and debugging using simple queries.Additional foundational skills in DevOps AI implementations :Familiarity with AI / ML model deployment pipelines using tools like MLflow, Kubeflow, or SageMakerBasic understanding of MLOpsprinciples and integration with CI / CD workflowsExperience with versioning and tracking of ML models and datasetsKnowledge of containerizing AI workloads and managing resource allocation for GPU / TPU environments.Awareness of data governance and compliance practices in AI-driven DevOps environments