GCP Data Engineer

Requirements

• experience with real-time analytics use cases,
• exposure to self-service data platforms / data products,
• knowledge of cost optimization in BigQuery,
• experience in large-scale enterprise transformations,
• GCP certifications (e.g. Professional Data Engineer),
• experience working in international distributed teams,
• experience applying GenAI in a more structured way within the SDLC, including defined workflows, prompt patterns, or tool integrations embedded into daily work,
• 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

Additional Information

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, building, and maintaining scalable data pipelines on GCP, developing and optimizing data models using BigQuery and dbt, building and managing real-time and batch data processing using Kafka, ensuring data quality, consistency, and reliability across pipelines, supporting migration of legacy data systems into modern cloud-based architecture, collaborating with Data Architects, Platform Engineers, and Analytics teams, enabling self-service data capabilities for internal consumers (data products mindset), implementing monitoring, logging, and observability for data pipelines, optimizing performance and cost of data workloads in GCP, contributing to best practices in data governance and data engineering standards. Your profile: strong hands-on experience with BigQuery, solid experience with dbt (data transformations, modeling), experience working with Kafka (streaming data pipelines), strong skills in building data pipelines (batch and/or streaming), very good knowledge of SQL, experience with cloud data platforms (preferably GCP), understanding of data modeling concepts (DWH, ELT/ETL), experience working in complex, multi-team environments, strong problem-solving skills and ownership mindset, good communication and collaboration skills, experience with data platform or data mesh concepts, familiarity with CI/CD for data workflows, knowledge of Python or Scala for data processing, experience with data quality / validation frameworks, understanding of data governance and security best practices, experience in cloud migration projects, familiarity with orchestration tools (e.g. Airflow, Cloud Composer), 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.

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