Lead Data Engineer, Customer Identity Analytics (hybrid - Seattle, Wa)

Nordstrom Nordstrom · Retail · Seattle, WA

Lead Data Engineer for Nordstrom's Customer Identity team, focusing on building and maintaining the data infrastructure for customer analytics. The role involves owning the technical direction, guiding a team, and improving customer recognition accuracy through data quality analysis, match accuracy improvements, and platform modernization. Key responsibilities include designing quality KPIs, migrating infrastructure to a warehouse-native architecture (BigQuery + dbt), and mentoring engineers.

What you'd actually do

  1. Own the strategy to improve customer recognition accuracy by working with upstream data partners, evaluating data enrichment approaches and piloting new techniques that help deliver more consistent and personalized experiences
  2. Collaborate with customer analytics leadership and business stakeholders to ensure the identity platform supports key use cases, from marketing measurement to customer lifetime value modeling to personalization
  3. Design and implement platform quality KPIs, match confidence scoring, precision/recall metrics, and reconciliation measures across identity methods to ensure accuracy and reliability
  4. Lead the migration of identity infrastructure from complex orchestration layers to a warehouse-native architecture (BigQuery + dbt), reducing operational overhead so the team can focus on analytical improvement
  5. Mentor and develop engineers on the customer analytics, setting technical standards, running design reviews, and providing direct feedback

Skills

Required

  • 7+ years of experience in data engineering, applied data science, or machine learning
  • hands-on work in entity resolution, record linkage, or customer identity systems at scale
  • Deep understanding of data matching concepts: match rules, blocking strategies, confidence thresholds, and precision/recall tradeoffs in customer data systems
  • Production-quality SQL skills
  • fluency in BigQuery or a comparable cloud data warehouse
  • hands-on experience in dbt, airflow or equivalent transformation frameworks
  • Ability to frame ambiguous data quality problems as measurable experiments
  • communicate statistical concepts to business stakeholders

What the JD emphasized

  • entity resolution
  • record linkage
  • customer identity systems at scale
  • data matching concepts
  • match rules
  • blocking strategies
  • confidence thresholds
  • precision/recall tradeoffs