Data Engineer

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.

The biggest challenge

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.

What you’ll do as Data Engineer

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 Why you can make a difference

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.

3 reasons why this is (not) for you Switch to find out -

Hands-On Overhead Realist: You want to spend most of your time on novel data modeling problems. A lot of this role is unglamorous plumbing: backfills, schema migrations, and chasing down why a metric moved 0.3%. -

Independent Builder Preference: You find stakeholder conversations draining and would rather be handed a spec. Here, the spec usually doesn’t exist yet - you help define it. -

Greenfield Seeker: You’re looking for a fresh rebuild. This is an existing platform with past decisions you’ll inherit, work within, and gradually improve. +

Data Impact Driver: Your work empowers teams across bol.com to make data-driven decisions by building pipelines that fuel A/B testing, marketing campaigns, and product analytics. +

Future-Proof Thinker: You prioritize long-term reliability over quick wins, designing pipelines that remain robust over time and proactively adapting models when requirements change. +

Data Integrity Advocate: You confidently challenge incorrect metrics and welcome feedback on your own work, fostering a culture of accuracy, transparency, and continuous improvement. This is where you’ll work

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., Discover all perks Flexible working

We bring the best of both worlds together by working 50% at the office and 50% at home. This way, we find a balance between organisational and individual needs.

Bonus

The bonus is calculated at the end of the year and we always end the year with a fun party!

The culture and the office

Our colleagues work hard to make the daily lives of our customers easier and more fun. But of course, we do this in an inspiring and creative environment!

Your application process Your application We’ll review your application with care. We aim to get in touch with you as soon as possible. First contact We’ll contact you to walk you through the process and take the first step to set up an interview. And since we’re already talking: feel free to ask any questions you may have. The assessment We will ask you to take an online HR assessment and a technical assessment. We’ll also discuss the position and the team in depth. First date During this interview we’ll get to know each other. We want to find out more about you, your work experience and skills. Is this love? Two interviews are usually enough to see if it’s a match. And if you agree… well, it’s the beautiful beginning of your career at bol. Any questions?

I’m Monika Myslinska, Recruiter at bol. Anything I can help you with regarding the Data Engineer vacancy?

Do you have experience in Scalability?, 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