By building the data pipelines that turn raw customer interaction events into reliable datasets that teams across bol use to understand and improve the shopper journey.

Every click, view, search, add-to-cart, and purchase that happens across bol’s platform generates an event. Those events need to be captured, validated, transformed, and made available to teams across advertising, marketing, experimentation, product analytics, and feature development. When the event data is accurate and trustworthy, teams can make better decisions.

Customer interaction data flows from multiple touchpoints across web, android and ios apps. Each source has its own quirks, schema evolution, and edge cases. Volumes are high, expectations for reliability and accuracy are even higher, and pipelines need to be correct, observable, and performant.

Data quality is the hardest part. Event schemas drift, upstream systems change, and anomalies need to be detected before they cascade downstream. You’ll design dbt models that encode event logic clearly, build monitoring and anomaly detection that catches issues early, orchestrate workflows in Airflow that handle failures gracefully, and write Python that holds up under production load. Testing, data quality checks, and documentation are part of the deliverable, not an afterthought.

You own the engineering side of the Shop Insights platform: event ingestion, transformation pipelines, data models, orchestration, and the conventions that keep it all maintainable. Tracking requirements and business needs change constantly, and the pipelines that serve them need to evolve without breaking trust in the data.

Day to day, you’ll:

• Design, build, and maintain dbt models that transform raw interaction events into clean, documented datasets
• Orchestrate workflows in Airflow with proper failure handling and observability
• Write clean, well-tested Python for event ingestion, transformation, and tooling
• Build and maintain data quality checks, anomaly detection, and monitoring to catch issues before they impact downstream teams
• Optimize pipelines for cost, latency, and scalability
• Partner with analysts, feature teams, and domain stakeholders to deliver reliable tracking solutions
• Contribute to team conventions: CI/CD, code review, documentation — that lift everyone’s work

Shop Insights provides the event tracking and measurement capabilities that teams across bol depend on. When the data is reliable and accurate, teams can trust their analyses, run confident experiments, and make better product decisions. When it’s not, trust in the numbers breaks down and teams hesitate to act on insights.

You’re building pipelines that enable teams — from experimentation to advertising to product analytics — to understand customer behavior and drive their businesses forward. The quality and stability of what you build directly impacts how confidently the organization can use data.

You’ll join the Shop Insights & Experimentation product group, working closely with feature teams across bol and collaborating with Advertising, Marketing, Ecommerce, Product Analytics, and Experimentation domains. The team plays a business-critical role as the provider of all customer interaction data across the organization.

To be successful in this role, you need:

• Strong proficiency in dbt for modeling and transforming analytical data
• Solid proficiency in Python for data engineering, tooling, and custom pipelines
• Experience building and operating workflows in Airflow (or similar orchestrators)
• Strong SQL and experience with cloud data warehouses (BigQuery a plus)
• Experience designing data models for reporting and analytical consumption (star schemas, marts, semantic layers)
• Experience with testing, CI/CD, and version control in data engineering contexts
• Strong focus on data quality, anomaly detection, and observability practices
• Ability to explain data architecture and trade-offs to non-technical stakeholders
• Experience with event-driven data architectures and streaming pipelines (pub/sub) is a plus