Staff Software Engineer - Data & AI Platforms [T500-28128]

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.

About this team:

The Enterprise Data & AI team is a strategic and operational driver of growth for lululemon, owning and building the data and AI platforms and products that enable the enterprise to operate with intelligence at scale. The team leads the design and delivery of a trusted unified data foundation, advanced analytics capabilities, and AI solutions across lululemon’s vertically integrated retail ecosystem, embedding strong data governance and responsible AI practices from the very beginning. By applying AI to critical business challenges and creating new, transformative AI solutions, the team helps reshape how lululemon operates. Through deep partnership with product, technology, and business teams, Enterprise Data & AI accelerates product innovation, unlocks measurable value, elevates guest and educator experiences, and drives enterprise efficiency.

Core responsibilities:

As a Staff Software Engineer, you will design and deliver data systems, AI-enabled platform capabilities, enterprise solutions and technical frameworks that span multiple teams, establishing architectural patterns for how enterprise knowledge is modeled, connected, and consumed. You will operate at the intersection of Data (logical and semantic), AI/LLM retrieval and grounding, and distributed systems and APIs. You will consult on architectural decisions in collaboration with enterprise architects providing technical expertise on system design, balance hands-on coding in critical areas with advisory responsibilities across teams, and ensure technical solutions deliver measurable business value. You will mentor engineers at all levels including emerging technical leaders, identify and resolve critical engineering challenges spanning multiple systems, and drive initiatives that improve developer productivity, code quality, and system reliability at organizational scale. This role may require availability outside of standard business hours, including on call support, based on business needs.

Select responsibilities include:

• Design and evolve canonical semantic models (taxonomies, ontologies, knowledge graphs) for enterprise domains such as product, customer, and supply chain
• Design systems that provide high-quality context for LLMs, Retrieval pipelines (RAG / GraphRAG), Semantic search (embeddings, hybrid search), evidence-based reasoning layers
• Partner with AI/ML Science teams to ensure model correctness to reduce errors, hallucination risk and improve trust
• Address challenges of data ambiguity, duplication, and inconsistencies at scale through systemic data validation, schema governance and versioning practices
• Design and implement enterprise software solutions and technical frameworks spanning multiple teams, establishing coding patterns and practices that scale across the organization
• Drive complex, multi-team initiatives with minimal supervision, making key technical decisions independently while navigating ambiguity and competing priorities
• Write exemplary code in critical areas demonstrating best-in-class engineering practices when situations require deep technical expertise
• Consult with principal engineers and enterprise architects on architectural decisions providing technical expertise on implementation feasibility, scalability considerations, and operational implications
• Recommend technology options and tools to architects based on technical evaluation, team capabilities, and implementation requirements
• Establish engineering and coding standards adopted across the organization including testing strategies, development workflows, and quality practices
• Lead technical design reviews across the organization providing expert guidance on implementation approaches, scalability, …