Expert Data Engineer

bol; Utrecht; On-site; EUR 90,000 - 120,000; Full time

Job summary

bol is seeking an Expert Data Engineer to join AdInsights in the advertising domain. You will build scalable data pipelines and models to deliver reliable advertiser insights via dashboards and APIs, tackling data from multiple sources and ensuring consistency and quality as the platform grows.

You will collaborate with engineers, analysts and product managers to drive data quality, observability and performance, aligning data contracts and focusing on maintainability and impact across bol's

Qualifications

• Expert-level SQL and Python skills, with strong experience in data modelling and analytical data products.
• Experience building and operating large-scale data pipelines, preferably with Airflow and dbt or comparable tooling.
• Strong understanding of cloud data warehouses; BigQuery experience is a plus.
• A strong focus on data quality, observability, performance, cost and maintainability.
• Ability to turn complex data from multiple sources into reliable, reusable datasets and metrics.
• Ability to influence technical decisions and explain trade-offs to engineers, analysts, product managers and stakeholders.
• Experience raising the data engineering craft level of a team.
• Experience with advertiser-facing data products, dashboards, APIs, Java or Kotlin is a plus.

Responsibilities

• Define and improve the technical approach for AdInsights data pipelines, models and aggregation logic.
• Solve complex data engineering challenges in combining advertising and commerce data from multiple sources.
• Improve data quality, observability, testing and operational reliability within the team.
• Help the team make better trade-off decisions around performance, scalability, cost and maintainability.
• Align with adjacent teams on shared datasets, metric definitions, data contracts and dependencies.
• Bring data engineering knowledge to other engineers and help raise the craft level of the AdInsights team.
• Act as a technical sparring partner for engineers, analysts and product managers when decisions depend on data engineering depth.
• Translate relevant data engineering practices and learnings into practical improvements for AdInsights.
• Take ownership of difficult technical problems while helping others make better technical decisions independently.

Skills

SQL

Python

Data modelling

Analytical data products

Stakeholder communication

Data quality

Observability

Performance

Cost management

Java/Kotlin

Tools

Airflow

dbt

BigQuery

Job description

Join our AdInsights team

By building the data products that help advertisers understand how their campaigns are performing. AdInsights brings together data from across the bol ecosystem, transforms it into reliable and meaningful advertising insights, and makes those insights available through advertiser-facing dashboards and APIs.

Your work enables advertisers to evaluate their results and make better-informed decisions about their campaigns.

The biggest challenge

Advertising insights are created by combining large amounts of data from different sources across bol. The challenge is to aggregate this data effectively and efficiently, and turn it into reliable insights that advertisers can access through dashboards and APIs.

You will build scalable data pipelines and models that ensure advertisers receive consistent and trustworthy information about their campaign performance. Data quality, maintainability and performance are essential, because advertisers rely on these insights to understand the value of their advertising investments.

What you will do as Expert Data Engineer

As an Expert Data Engineer in AdInsights, you are a deep technical expert within the team. You take on the most complex data engineering challenges behind advertiser insights and help the team make strong choices in data modelling, pipeline design, quality, observability and scalability. Your scope is primarily within AdInsights, while you actively align with adjacent teams when shared data definitions, dependencies or reporting consistency require it.

You work on the hardest AdInsights data problems: combining data from many sources, improving metric consistency, strengthening data quality, and making sure advertiser-facing dashboards and APIs remain reliable as the advertising business grows. You look beyond the immediate implementation and translate your experience into pragmatic improvements that help the team build better data products.

• Define and improve the technical approach for AdInsights data pipelines, models and aggregation logic


• Solve complex data engineering challenges in combining advertising and commerce data from multiple sources


• Improve data quality, observability, testing and operational reliability within the team


• Help the team make better trade-off decisions around performance, scalability, cost and maintainability


• Align with adjacent teams on shared datasets, metric definitions, data contracts and dependencies


• Bring data engineering knowledge to other engineers and help raise the craft level of the AdInsights team


• Act as a technical sparring partner for engineers, analysts and product managers when decisions depend on data engineering depth


• Translate relevant data engineering practices and learnings into practical improvements for AdInsights


• Take ownership of difficult technical problems while helping others make better technical decisions independently

Why you can make a difference

Advertisers need confidence that the numbers they see are complete, consistent and timely. In this role, you make that possible by building the data foundations that turn complex source data into metrics they can trust.

The quality of the pipelines and models you build directly affects the trust advertisers place in our dashboards and APIs. By making advertising data accurate, consistent and accessible, you help advertisers make better decisions and improve the value they get from advertising on bol.

To be successful as Expert Data Engineer, you need:

• Expert-level SQL and Python skills, with strong experience in data modelling and analytical data products


• Experience building and operating large-scale data pipelines, preferably with Airflow and dbt or comparable tooling


• Strong understanding of cloud data warehouses; BigQuery experience is a plus


• A strong focus on data quality, observability, performance, cost and maintainability


• Ability to turn complex data from multiple sources into reliable, reusable datasets and metrics


• Ability to influence technical decisions and explain trade-offs to engineers, analysts, product managers and stakeholders


• Experience raising the data engineering craft level of a team


• Experience with advertiser-facing data products, dashboards, APIs, Java or Kotlin is a plus

3 reasons why this is (not) for you

You are likely to enjoy this role if:

• Advertising Impact Driver — Your work directly helps advertisers understand campaign performance and make informed decisions.


• Data Puzzle Solver — You enjoy bringing together data from different sources and turning it into clear, reliable insights.


• Quality-Minded Builder — You care about scalable engineering, trustworthy metrics and solutions that remain maintainable as the platform evolves.

This role may be less suitable if:

• You prefer working with simple datasets from one well-defined source. This role involves combining data from across a complex ecosystem.


• You would rather receive a complete specification and work mostly on your own. Here, you collaborate to clarify reporting needs and shape the solution.


• You are mainly interested in visual reporting. This role focuses on the data engineering foundations that power both dashboards and APIs.

Where you'll be working

You’ll join AdInsights, a team in the advertising domain that builds the data foundations behind advertiser insights. The team works on the pipelines, models and services that turn complex advertising and commerce data into reliable campaign performance information.

You’ll collaborate closely with engineers, analysts, product managers and advertising stakeholders to translate reporting needs into data products that are accurate, scalable and useful in practice.