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Data Engineer / Data Steward

Skills

analyticsbigquerycloudcorrective actiondata engineeringdata governancedata qualitydata warehousingdatabricksdbtgcpmetadata managementpysparkpythonroot cause analysissql

Description

Overview

In this role, you will build and govern data products by developing robust ingestion and transformation pipelines and defining data quality rules. You will drive governance standards, metadata, and data definitions to improve discoverability and reliability. You will collaborate with product, engineering, and data teams to translate requirements into scalable data solutions and governance practices. This position sits at the intersection of data engineering, analytics engineering, and data stewardship, offering impact through better data usability and trust. Amsterdam-based with partial onsite work, offering a meaningful opportunity to shape data foundations at scale.

Verantwoordelijkheden
  • Build, improve, and maintain ingestion and transformation pipelines using SQL and Python
  • Perform ad hoc analytics when required
  • Define and maintain data quality rules, tests, and dashboards using dbt and Elementary
  • Lead root-cause analysis for data quality issues and drive corrective actions
  • Maintain business definitions, harmonisation rules, and metadata across datasets
  • Review data models, data contracts, and data mappings for governance alignment
  • Present data product, governance, and data quality insights to stakeholders
  • Collaborate with data engineers, data stewards, software engineers, and product teams
Belangrijkste vereisten
  • Strong data engineering fundamentals with pipelines in dbt or Python
  • Production-grade cloud development experience (GCP preferred)
  • Hands-on experience with BigQuery and/or PySpark/Databricks
  • Strong SQL skills
  • Experience with dbt modelling and data quality testing
  • Familiarity with data quality frameworks and methodologies
  • Knowledge of data models, databases, data warehousing, and data flows
  • Desirable: experience with customer data (profiles, purchase events, interaction data)
  • Understanding of data governance, metadata management, and data cataloguing
  • Strong collaboration across business andTechnical stakeholders
  • Clear communication of findings via dashboards and collaborative tools
  • Problem-solving with ownership and resolution focus
  • dbt modelling
  • Python
  • BigQuery

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