Sr. Principal Engineer - Data Platform - Hybrid - Seattle, WA

Nordstrom•Seattle, WA

5d•Hybrid

About The Position

At Nordstrom, we're committed to delivering outstanding customer experiences by harnessing the power of data. We are seeking a Sr. Principal Engineer - Enterprise Data to join our Technology team. In this pivotal role, you will define and drive enterprise-wide data architecture strategy, governance frameworks, and standards that enable Nordstrom's continued evolution as a digital-first omnichannel retailer. This is a strategic, influence-driven role that will shape how engineering teams design, govern, and consume data for years to come. You will collaborate with executives and leaders across all disciplines to translate business strategy into data strategy, establishing the architectural foundations that enable analytics excellence, operational efficiency, and AI readiness. If you're passionate about enterprise data architecture and ready to make a lasting impact, we want to hear from you. A day in the life... Define Enterprise Data Strategy: Develop and communicate Nordstrom's enterprise data architecture vision, creating multi-year roadmaps aligned with business priorities, cloud strategy, and AI ambitions. Establish Standards and Reference Architectures: Own enterprise data architecture standards, reference designs, and design patterns spanning multiple domains. Create conceptual and logical data models, naming conventions, and metadata standards that guide domain team implementations. Lead Data Governance and Quality: Establish holistic enterprise data governance spanning source data stores, event streams, and analytics platforms. Define data contracts between producers and consumers, quality rules, stewardship responsibilities, and quality SLOs. Build the enterprise business glossary and data dictionaries. Ensure Privacy, Security, and Compliance: Incorporate privacy-by-design principles aligned with evolving regulations. Define data classification policies, access control standards, and security architecture patterns. Enable Analytics and AI Readiness: Design data environments supporting traditional BI, self-service analytics, and AI-powered data access. Define semantic layer specifications bridging technical schemas and business concepts, enabling conversational analytics and natural language interfaces. Support MLOps infrastructure including feature stores, data versioning, and ML pipeline requirements. Build Data Culture and Talent: Advocate for data-driven approaches across the organization. Facilitate communities of practice for knowledge sharing. Coach and mentor data architects, engineers, and analysts, advising on resourcing and promotions into technical leadership roles.

Requirements

Extensive Experience: 15+ years in data architecture, data engineering, or enterprise architecture roles, with demonstrated progression to strategic, enterprise-scope positions.
Data Modeling Mastery: Deep expertise across conceptual, logical, and physical data modeling with proven ability to create abstractions that guide implementation teams.
Governance Framework Fluency: Strong grounding in industry data management frameworks and experience adapting them to organizational context.
Strategic Influence: Demonstrated ability to lead through influence rather than authority, building consensus and driving adoption across organizational boundaries.
Platform Proficiency: Expertise with cloud platforms, streaming architectures, and modern distributed data technologies sufficient to evaluate tradeoffs and guide architectural decisions.
Business Acumen: Ability to connect data architecture to business outcomes, build compelling business cases, and communicate value to executive stakeholders.
Adaptive Mindset: Track record of evolving architectural approaches as technology landscapes and organizational needs change.
Collaborative Leadership: Experience building cross-functional partnerships and developing technical talent across teams you don't directly manage.

Responsibilities

Define Enterprise Data Strategy: Develop and communicate Nordstrom's enterprise data architecture vision, creating multi-year roadmaps aligned with business priorities, cloud strategy, and AI ambitions.
Establish Standards and Reference Architectures: Own enterprise data architecture standards, reference designs, and design patterns spanning multiple domains. Create conceptual and logical data models, naming conventions, and metadata standards that guide domain team implementations.
Lead Data Governance and Quality: Establish holistic enterprise data governance spanning source data stores, event streams, and analytics platforms. Define data contracts between producers and consumers, quality rules, stewardship responsibilities, and quality SLOs. Build the enterprise business glossary and data dictionaries.
Ensure Privacy, Security, and Compliance: Incorporate privacy-by-design principles aligned with evolving regulations. Define data classification policies, access control standards, and security architecture patterns.
Enable Analytics and AI Readiness: Design data environments supporting traditional BI, self-service analytics, and AI-powered data access. Define semantic layer specifications bridging technical schemas and business concepts, enabling conversational analytics and natural language interfaces. Support MLOps infrastructure including feature stores, data versioning, and ML pipeline requirements.
Build Data Culture and Talent: Advocate for data-driven approaches across the organization. Facilitate communities of practice for knowledge sharing. Coach and mentor data architects, engineers, and analysts, advising on resourcing and promotions into technical leadership roles.