GCP Analytics Engineer

Xebia is a global AI-first, digital transformation, and engineering partner. With over 25 years of experience and a team of 5,000 professionals across 16 countries, we help organizations design and build scalable products, platforms, and data-driven solutions. 

We specialize in Artificial Intelligence, Data and Cloud, Intelligent Automation, and Digital Products, combining deep technical expertise with a strong focus on engineering excellence and a people-first culture.  

In the CEE region, we’re a team of nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for clients such as Millennium, ING, Play, Edenred, Arabian Drilling, FedEx, Leroy Merlin, Truecaller, Volotea, Schmitz Cargobull, and many, many more. We work with leading technologies including AWS, Azure, GCP, Databricks, and Snowflake, and combine strong engineering culture with a consulting mindset and a continuous focus on growth and knowledge sharing. 

You will:

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  design, develop, and maintain dbt-based transformation frameworks,

  
  


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  build and optimize data models supporting Silver and Gold data layers,

  
  


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  optimize dbt execution performance and operational efficiency,

  
  


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  support versioning and lifecycle management of dbt artifacts,

  
  


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  separate dbt models from runtime Docker images and implement controlled distribution mechanisms,

  
  


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  evaluate and implement alternative execution approaches using native BigQuery capabilities where appropriate,

  
  


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  consolidate and simplify data transformation workflows,

  
  


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  collaborate with Data Engineering, DevOps, and Platform teams,

  
  


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  support data quality and transformation governance initiatives,

  
  


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  prepare and maintain technical documentation.

  
  

Your profile:

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  strong commercial experience with dbt,

  
  


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  strong SQL skills,

  
  


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  hands-on experience with BigQuery,

  
  


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  experience designing and building analytical or transformation layers on modern data platforms,

  
  


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  experience developing and maintaining data models for enterprise analytics environments,

  
  


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  understanding of data model lifecycle management and versioning,

  
  


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  experience with Docker-based deployment approaches,

  
  


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  familiarity with Python,

  
  


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  experience optimizing the performance and operational efficiency of data transformations,

  
  


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  strong communication and documentation skills.

  
  

Nice to have:

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  experience with Apache Airflow,

  
  


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  experience with BigQueryInsertJobOperator or similar orchestration approaches,

  
  


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  familiarity with Google Cloud Storage and Kubernetes,

  
  


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  experience building reusable transformation frameworks,

  
  


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  experience optimizing cloud processing costs,

  
  


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  experience in regulated industries, including banking or financial services,

  
  


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  experience supporting data quality and governance initiatives.

  
  

Recruitment Process:

CV review – HR call – Interview – Client Interview – Decision