Data Engineer 2 - Customer Insights Delivery (hybrid - Seattle, Wa)

Nordstrom Nordstrom · Retail · Seattle, WA

Data Engineer role focused on modernizing customer insights data pipelines using dbt and a semantic layer, with an emphasis on integrating AI tools for development efficiency and reducing toil. The role involves building and maintaining production SQL/dbt models, migrating transformations, and partnering with analytics and data science teams.

What you'd actually do

  1. Build and maintain production dbt models and performant SQL that power customer, marketing, and loyalty analytics across Nordstrom.
  2. 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.
  3. 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.
  4. 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.
  5. 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.

Skills

Required

  • 2+ years writing production-quality SQL (joins, aggregations, window functions, query optimization)
  • Fluency in BigQuery or comparable cloud data warehouse
  • 1+ year of experience building dbt models, or strong familiarity with dbt fundamentals
  • 1+ year of experience in Python or Java
  • 1+ year of experience with data modeling and data warehousing
  • Exposure to cloud environments (GCP or AWS)

Nice to have

  • Experience with semantic layer tools (e.g., Looker)
  • Experience with Airflow or similar orchestration tools
  • Familiarity with distributed data processing tools (e.g., Kafka or Spark)
  • Experience using LLM-assisted tools (e.g., Claude Code, Cursor) in an analytics workflow

What the JD emphasized

  • production dbt models
  • dbt migration
  • semantic layer
  • AI in your day-to-day workflow
  • LLM-assisted development
  • agent-built pipeline scaffolding

Other signals

  • dbt modernization
  • semantic layer
  • AI in data pipelines