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Data Engineer

Skills

aibigqueryci cdcloudcontainerizationdata engineeringdata governancedata qualitydata visualizationdata warehousingdatabricksdbtgcpinfrastructure as codelooker studiometadata managementpowerbipysparkpythonPython

Description

  • Strong data engineering fundamentals, incl but not limited to writing pipelines in dbt and Python and production-grade code on cloud platforms (GCP preferred), CI/CD, IaC, containerisation
  • Hands-on experience with BigQuery and Databricks/PySpark (at least one is required, but preference will be given to those with both)
  • Ability to review and improve processes by introducing new ways of e.g. coding, reviewing, testing through smart (not sloppy!) use of AI
  • Basics in data visualisation (ideally Data Studio, PowerBI would be a big plus)
  • Experience with data quality frameworks: defining rules, writing tests, investigating failures
  • Knowledge of data structures: data models, databases, data warehousing, and how data flows across systems
  • Experience with customer data in general (profile data, events such as purchases and interactions) is a strong plus
  • Experience with classifications and unified profile data products is a strong plus
  • Knowledge of data governance frameworks, metadata management, and data cataloguing best practices is a plus
  • Communication and Interpersonal Skills
  • Perseverance and proactive coping with uncertainty: we still have a lot to figure out and need someone excited about that rather than overwhelmed
  • Collaboration: strong communication skills to work with Country Data Management Leads, Product Owners, Data Engineers, and Software Engineers
  • Constructive problem-solving: ability to assess data issues, identify owners, and drive resolution through stakeholder collaboration
  • Ability to translate business requirements into data products, data quality rules, and governance standards

What 3 things from the box above are most important?

  • Strong data engineering fundamentals, incl but not limited to writing pipelines in dbt and Python and production-grade code on cloud platforms (GCP preferred), CI/CD, IaC, containerisation
  • Hands-on experience with BigQuery and Databricks/PySpark (at least one is required, but preference will be given to those with both)
  • Perseverance and proactive coping with uncertainty: we still have a lot to figure out and need someone excited about that rather than overwhelmed.

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