Senior GCP Data 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 McLaren, Aviva, Deloitte, Spotify, Disney, ING, UPS, Tesco, Truecaller, AllSaints, Volotea, Schmitz Cargobull, Allegro, InPost, 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 be:

• designing, implementing, and migrating large-scale data pipelines to GCP, with BigQuery as a core analytics layer,


• building and maintaining production-grade batch and streaming data pipelines


• validating performance, reliability, scalability, and cost efficiency of migrated workloads,


• actively contributing to the evolution of a modern data processing stack (cloud-native, scalable, and observable),


• working closely with platform and infrastructure engineers on shared data foundations,


• supporting and improving data platform components used across multiple teams and business domains,


• taking ownership of delivered solutions and ensuring they meet real production requirements.

Your profile:

• strong hands-on experience in Data Engineering or backend engineering with a clear data focus,


• very good knowledge of Java, used in data-intensive or platform-related contexts


• solid experience with GCP, especially BigQuery, in real production environments,


• very good understanding of SQL and analytical data modeling,


• practical experience with at least one data processing framework (e.g. Beam, Dataflow, Spark, Flink),


• experience building and operating production-grade data pipelines (batch and/or streaming),


• familiarity with containerized applications and basic Kubernetes concepts,


• understanding of cloud infrastructure and DevOps practices as part of daily engineering work,


• ability to write high-quality, maintainable code and deliver reliably to production,


• high level of autonomy, ownership, and ability to work effectively in ambiguous environments,


• experience with platform engineering or building shared data/platform capabilities,


• exposure to Scala and/or Python in data engineering pipelines,


• experience working in large, distributed engineering organizations,


• understanding of data platform observability, monitoring, and operational aspects,


• practical experience using AI-powered assistants (e.g. Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making in software delivery.

Work from the European Union region and a work permit are required.

Nice to have:

• experience with Apache Flink (especially in streaming use cases),


• previous involvement in large-scale data pipeline migrations,


• familiarity with cost optimization for cloud-based data processing workloads,


• experience with Lakehouse-style architectures and modern data platforms,


• interest in and familiarity with emerging AI-driven practices (e.g. agent-based workflows, automation patterns, AI-augmented development), with a willingness to explore and experiment beyond standard approaches.

Recruitment Process:

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