Data Engineer 2 - Customer Insights Delivery (Hybrid - Seattle, WA)

The Customer Insights Delivery team owns the trusted data behind how Nordstrom understands its customers and marketing programs — spanning customer behavior, customer lifecycle, customer care, marketing measurement, loyalty, and gift card programs, among other critical domains. The decisions our datasets drive show up in personalization, marketing spend, and loyalty program decisions every day. We are in the middle of a meaningful modernization: establishing dbt as the core of our analytics engineering stack, investing in a semantic layer, and expanding the role of AI in how our pipelines are built and maintained. 

This role reports to the Senior Engineering Manager, Customer Insights Delivery, and partners day-to-day with Customer Analytics, Marketing, and Customer Care teams. You will join a team of 12, with hands-on opportunities to contribute to the dbt migration of around 40 critical pipelines across domains and semantic-layer work alongside more senior engineers as you grow in the role. 

Read this first: We encourage you to apply even if you do not meet all the qualifications but feel you would be a good fit for the position. Nordstrom is an equal-opportunity employer. We are building a diverse, inclusive workforce and hope you will join us. 

This role is offered as hybrid in Seattle, WA. Candidates must be available to work in office at the Nordstrom corporate headquarters a minimum of 4 days/week to be considered for this position.

A day in the life... 

• Build and maintain production dbt models and performant SQL that power customer, marketing, and loyalty analytics across Nordstrom. 

• Contribute to the dbt and semantic-layer modernization by migrating transformations, writing tests and documentation, and learning the patterns that shape our analytics engineering stack. 

• Use AI in your day-to-day workflow, including LLM-assisted development, agent-built pipeline scaffolding, and emerging tools, to ship faster and reduce toil. 

• Partner with Customer Analytics and Data Science teams to deliver trusted datasets that feed marketing campaigns, personalization models, customer health metrics, and downstream analytical applications. 

• Participate in code reviews, apply data engineering best practices (dbt testing, documentation, lineage, performance, and cost), and grow your skills through feedback from senior engineers. 

• Participate in on-call and production support, monitor your pipelines, and collaborate with Customer Analytics and Data Science teams to deliver effective self-service BI. 

You own this if you have...

• 2+ years writing production-quality SQL — including joins, aggregations, window functions, and query optimization — with fluency in BigQuery or a comparable cloud data warehouse. 

• 1+ year of experience building dbt models, or strong familiarity with dbt fundamentals (models, tests, documentation, project structure). 

• 1+ year of experience in at least one modern programming language such as Python or Java. 

• 1+ year of experience with data modeling and data warehousing. 

• Exposure to cloud environments such as GCP or AWS. 

• Curiosity about applying AI and ML to data engineering and business problems, and a passion for operational excellence and customer impact. 

Nice to have... 

• Experience with semantic layer tools such as Looker. 

• Experience with Airflow or similar orchestration tools. 

• Familiarity with distributed data processing tools such as Kafka or Spark. Useful context, though not central to this role. 

• Experience using LLM-assisted tools such as Claude Code or Cursor in an analytics workflow.