Business Intelligence Engineer, Customer Data & Analytics, Customer Strategy

Business Intelligence Engineer, Customer Data & Analytics, Customer Strategy

Shopbop and Zappos are looking for a customer‑obsessed Business Intelligence Engineer to join the Customer Analytics organization. This role will own the data infrastructure and customer tables that power our customer strategy — designing and scaling the customer attribute store, defining data standards, and ensuring our teams have the reliable, well‑governed data foundation they need to drive decisions.

You will be the connective tissue between our data science, customer insights, and marketing teams — architecting the data assets that enable segmentation, personalization, and measurement  will define the strategy for integrating third‑party data sources with first‑party customer data, partner directly with Data Engineering to build and maintain scalable pipelines, and work closely with Customer Insights Managers and Data Scientists to operationalize their work and quantify its impact.

The right candidate combines strong data engineering fundamentals with strategic thinking and business judgment. They take ownership of the full data lifecycle — from pipeline architecture and table design through to the dashboards and reporting that inform leadership decisions. They operate with a high degree of autonomy, proactively identify gaps in our data infrastructure, and influence the roadmap to close them.

They should have a collaborative mindset that enables them to work effectively across Customer Insights, Data Science, Lifecycle Marketing, Finance, and Engineering.

• Own the customer tables and customer attribute store end‑to‑end, defining schema design, data quality standards, and governance that ensure completeness, accuracy, and accessibility across Shopbop and Zappos.
• Partner with Data Engineering to architect scalable data pipelines, define SLAs for freshness and reliability, and drive the technical roadmap for the customer data layer.
• Build and maintain dashboards and reporting tools that enable Customer Insights, Marketing, and Leadership to track customer KPIs, segment performance, and strategy effectiveness.
• Lead deep dives with Customer Insights Managers to segment customers, measure lifecycle and acquisition impact, and surface actionable trends.
• Own third‑party data sources and tooling, ingestion cycles, compliance, and integrations connecting external data into our customer ecosystem.
• Define and execute the strategy for integrating third‑party data into our ecosystem.
• Partner with Data Science to operationalize models by designing feature tables, pipeline architecture, and reporting layers that bring predictive work into production.
• Drive measurement infrastructure by building foundations for holdout analysis, test/control reporting, and performance tracking.
• Develop reusable frameworks and self‑service tools that reduce ad‑hoc burden and scale the team’s analytical capacity.
• Identify gaps in the customer data, influencing priorities across teams to improve analytical infrastructure.

• 10+ years of professional or military experience.
• 5+ years of SQL experience.
• 1+ years of processing large, multi‑dimensional datasets from multiple sources.
• Experience programming to extract, transform, and clean large (multi‑TB) data sets.
• Experience with the theory and practice of design of experiments and statistical analysis of results.
• Experience with AWS technologies.
• Experience in scripting for automation (e.g. Python) and advanced SQL skills.
• 3+ years of developing automated reporting experience.
• Experience working directly with business stakeholders to translate between data and business needs.
• Experience managing, analyzing, and communicating results to senior leadership.
• Knowledge of data warehousing and data modeling.

• Experience with data pipeline and orchestration tools such as Airflow, dbt, or AWS Glue.
• Experience integrating third‑party data sources with first‑party customer data.
• Experience with customer segmentation frameworks and lifecycle analytics.
• Experience with Python or R for data analysis and automation.
• Understanding of data governance, data quality monitoring,…