> Markdown version of [/jobs/ext/4616427-principal-software-engineer-data-and-ai-platforms](https://www.wearedevelopers.com/jobs/ext/4616427-principal-software-engineer-data-and-ai-platforms). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Software Engineer, Data and AI Platforms - **Company:** LULULEMON - **Location:** Seattle, WA, United States - **Salary:** $205,600 - $269,900 - **Contract:** Permanent contract - **Skills:** Training Data, Application Programming Interfaces (APIs), Artificial Intelligence, Big Data, Configuration Management, Information Systems, Continuous Integration, Data Governance, Data Sharing, Data Systems, Distributed Systems, Graph Database, DataOps, Software Engineering, Systems Architecture, Enterprise Data Management, System Availability, Large Language Models, Knowledge Representation, Event Driven Architecture, Build Management, AI Platforms, Kubernetes, Information Technology, Production Code, Data Analytics, Integration Frameworks, Data Management, Restful APIs, Terraform, Data Pipelines - **Published:** November 7, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=06c15ba3839b451d ## About the Role * Bachelor's degree in Computer Science, Information Systems, Information Technology, or equivalent experience; Master's preferred * 11-15 years of progressive software engineering experience with demonstrated expertise in large-scale system design, technical architecture, and cross-functional leadership related to data systems and platforms or equivalent technology domains * Expert-level coding ability with a track record of writing production code to solve the most critical and complex technical challenges across multiple languages and frameworks * Experience building and governing shared data APIs and platform services (gRPC/REST APIs, SDKs, CLIs, and simple UIs) enabling consistent, scalable downstream consumption across analytics, AI/ML, and operational use cases * Experience designing both batch and streaming architectures and clear judgement on when each is the right answer * Deep understanding of big data and distributed systems challenges including partitioning, shuffle behaviour, skew, idempotency and backfills * Expertise in configuration management with a framework such as Kubernetes, Helm, Terraform etc. * Expertise in managing distributed and autoscaled applications in Kubernetes clusters * Proven experience owning enterprise data lake architecture end-to-end, including ingestion frameworks, pipeline orchestration, DataOps disciplines (CI/CD, SLAs, observability), platform reliability, and security, privacy, and access control in partnership with risk and compliance teams * Deep experience analyzing complex system interactions to identify optimal solution approaches; proven ability to make technical trade-off decisions balancing immediate delivery needs with long-term scalability and organizational impact * Experience defining implementation plans and execution approaches for complex, cross-organizational technology programs; familiarity collaborating with architects to translate architectural designs into actionable engineering roadmaps * Recognized depth in engineering implementations, combined with breadth to assess system-wide implications; proven ability to make rapid, high-confidence judgements on when to engage directly with engineers versus delegating * Demonstrated ability to influence product strategy and investment decisions through technical insights on feasibility, risk, and business impact * Experience defining enterprise semantic layers and canonical models - including taxonomies, ontologies, and knowledge graphs - used across multiple product teams and business domains * Exposure to AI-driven systems including optimization, predictions, and Agents; experience working with knowledge representation for LLM-powered applications * Experience building knowledge bases or data platforms in retail or similarly complex consumer domains must haves * Acknowledge the presence of choice in every moment and take personal responsibility for your life. * Possess an entrepreneurial spirit and continuously innovate to achieve great results. * 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. * Integrate fun and joy as a way of being and working, aka doesn't take yourself too seriously. ## Description The Enterprise Data & AI organization is a strategic and operational driver of growth for lululemon, owning and building the data and AI platforms and services that enable the enterprise to operate with intelligence at scale. The team leads the design and delivery of a trusted unified data foundation, AI-driven data analytics and insights, and AI solutions across lululemon's vertically integrated retail ecosystem, while 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. Our data and AI platform foundation is built on enterprise data lake architecture, standardized ingestion and integration frameworks, and canonical semantic models spanning product, customer, and supply chain domains. The platform advances data insights and AI capabilities while maintaining rigorous data governance, observability, and platform reliability. The team delivers self-service data capabilities and shared platform services that enable consistent, trustworthy consumption across analytics, AI/ML, and operational use cases at enterprise scale. core responsibilities As a Principal Software Engineer, you will own the system design and implementation of lululemon's enterprise data & AI platform and the systems that ensure delivery of data products and services that power high-impact business data-driven insights and AI use cases. You will ensure data pipelines and services that enable analytics, AI/ML, are reliable, efficient, and governed, and support operational consumption at scale. You will design and build enterprise-grade data and AI infrastructure and reusable components that enable multiple product teams and business domains to build, publish and consume data products. You will build a context and semantic layer infrastructure that enables insights and AI use-cases. You will enable and influence the engineering delivery of data insights and analytics across multiple use-cases. You will establish the direction and system design for the enterprise data platform, set engineering standards and platform practices adopted across the domain, and serve as the definitive technical authority on the enterprise data platform. You will partner closely with product, business, and technology stakeholders including VP-level leadership, on technical strategy, platform roadmap, and cross-functional initiatives. This role may require availability outside of standard business hours, including on-call support, based on business needs. select responsibilities include: * Define vision and approach for enterprise data initiatives and effectively and efficiently deliver the technology solution design * Collaborate closely with architects on enterprise data platform technical architecture and system design, and provide deep technical expertise on implementation approaches, feasibility assessment, and operational implications * Be accountable for their reliability, cost efficiency and adoption * Design and build foundational platform components that other engineering teams build on top of, across areas like ingestion, transformation, orchestration, compute and consumption * Build platform components for ML/AI applications such as point-in-time correct training data, online and offline feature parity or backfilling features at scale * Architect robust multi-tenant platform standards by establishing clear cross-layer contracts, tenant isolation, and SLOs to enable autonomous producers and consumers, while driving compute and storage efficiency across workloads * Lead cross team technical initiatives and architecture reviews, influence platform strategy with senior stakeholders and act as a technical mentor and multiplier for Senior engineers * Write high-quality production code to solve the complex technical challenges when hands-on expertise is required * Challenge and improve existing architecture decisions by identifying limitations in current systems and proposing technical solutions based on system design expertise * Provide executive-level technical advisory on complex implementation challenges and technology recommendations focused on build vs. buy decisions, technology selection, and implementation risk assessment * Drive engineering practices and technical workflows adopted company-wide, shaping how the organization builds and operates technology and increasing engineering throughput * Drive engineering velocity and throughput improvements across multiple teams through process optimization, tooling enhancements, and removal of systemic blockers