Senior GCP Data Engineer

O projekcie: Who We Are While Xebia is a global tech company, our journey in CEE started with two Polish companies – PGS Software, known for world-class cloud and software solutions, and GetInData, a pioneer in Big Data. Today, we’re a team of 1,000+ experts delivering top-notch work across cloud, data, and software. And we’re just getting started. What We Do We work on projects that matter – and that make a difference. From fintech and e-commerce to aviation, logistics, media, and fashion, we help our clients build scalable platforms, data and AI solutions, and cutting-edge applications to shape the future of tech. Our clients include McLaren, Aviva, Deloitte, Spotify, Disney, ING, UPS, Tesco, Truecaller, AllSaints, Volotea, Schmitz Cargobull, Allegro, InPost, and many, many more. We value smart tech, real ownership, and continuous growth. We use modern, open-source stacks, and we’re proud to be trusted partners of Databricks, dbt, Snowflake, Azure, GCP, and AWS. Fun fact: we were the first AWS Premier Partner in Poland! Beyond Projects What makes Xebia special? Our community. We support tech communities, organize meetups (Software Talks, Data Tech Talks), and have a culture that actively support your growth via Guilds, Labs, and personal development budgets — for both tech and soft skills. It’s not just a job. It’s a place to grow. What sets us apart?  Our mindset. Our vibe. Our people. And while that’s hard to capture in text – come visit us and see for yourself. Wymagania: 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. Codzienne zadania: - 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.