Senior Data Engineer

<p class="x_x_MsoNormal" data-olk-copy-source="MessageBody">Xebia is a global AI-first, digital transformation, and engineering partner.<span lang="EN-US">&nbsp;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.</span>&nbsp;</p> <p class="x_x_MsoNormal"><span lang="EN-US">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.&nbsp;</span>&nbsp;</p> <p class="x_x_MsoNormal"><span lang="EN-US">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.</span>&nbsp;</p> <p>You will join a central data platform / analytics team responsible for building and scaling batch data pipelines powering BI, reporting and machine learning use cases. The environment is cloud-based with a modern data stack (warehouse + transformation layer) and high focus on data quality and governance.</p> <p>&nbsp;</p> <h2><strong>You will be:</strong></h2> <p>Design, build and maintain scalable batch data pipelines<br>Develop and optimize ETL/ELT processes using SQL-first approach<br>Model and transform data into clean, consumable datasets (analytics-ready)<br>Ensure data quality, lineage, governance and observability across pipelines<br>Own data warehouse transformations and semantic layers (e.g. dbt)<br>Collaborate with product, analytics and data science teams to deliver datasets<br>Monitor, debug and optimize data processing jobs<br>Drive engineering standards, code reviews and best practices</p> <h2 data-pm-slice="1 1 []"><strong>Your profile:</strong></h2> <p>5+ years of experience in Data Engineering<br>Strong SQL skills and experience with relational and analytical databases<br>Proven experience building batch data pipelines (ETL/ELT)<br>Experience with modern data stack (e.g. dbt, Airflow or similar orchestration tools)<br>Hands-on experience with AWS<br>Solid understanding of data modeling (dimensional modeling, DWH concepts)<br>Experience ensuring data quality and building reliable pipelines</p> <p><strong>Should have</strong></p> <ul> <li>Experience with large-scale data processing (Spark or similar)</li> <li>Familiarity with data governance, lineage and observability tools</li> <li>Experience working with BI tools and analytics use cases</li> <li>Experience working in cross-functional teams (Product / Analytics / DS)</li> <li>Exposure to CI/CD for data pipelines</li> <li><span data-olk-copy-source="MessageBody">Practical experience using AI-powered assistants (e.g. Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making in software delivery.</span></li> <li> <p><strong>Work from the European Union region and a work permit are required.</strong></p> </li> </ul> <h2><strong>Nice to have:</strong></h2> <ul> <li>Experience with lakehouse architectures (Delta, Iceberg, etc.)<br>Knowledge of cost optimization in batch data workloads<br>Exposure to real-time or event-driven architectures<br>Experience mentoring or leading other engineers<strong><br></strong></li> <li> <p><span data-olk-copy-source="MessageBody">Experience applying GenAI in a more structured way within the SDLC, including defined workflows, prompt patterns, or tool integrations embedded into daily work.</span></p> </li> <li><span data-olk-copy-source="MessageBody">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.</span></li> </ul> <p>&nbsp;</p> <h2><strong>Recruitment Process:</strong></h2> <p><strong>CV</strong> review –<strong> HR</strong> call – <strong>Interview</strong> – <strong>Client </strong>Interview – <strong>Decision</strong></p> <p>&nbsp;</p>