Senior Data Engineer (AWS)

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 and implementing scalable data pipelines ingesting data from DynamoDB, Aurora PostgreSQL, and Neptune,
• building and maintaining data lake layers, including Raw, Canonical, and Curated zones on Amazon S3 using Apache Iceberg,
• developing ingestion frameworks supporting both CDC and batch processing patterns,
• contributing to the implementation and ongoing maintenance of the AWS Glue Data Catalog,
• developing and maintaining Airflow workflows for orchestration of data pipelines,
• implementing CI/CD practices for data platform development and deployment,
• automating testing, validation, and deployment processes across environments,
• ensuring reliable operation of development, staging, and production environments,
• optimizing query performance, storage layouts, and Iceberg table design,
• troubleshooting and resolving production issues while continuously improving platform performance and stability.

Your profile:

• 5+ years of experience in Data Engineering,
• strong Python development skills,
• advanced SQL proficiency,
• hands-on experience with AWS-based data platforms (Lake Formation, Athena, Glue, DynamoDB),
• experience building and maintaining data pipelines in Apache Airflow,
• commercial experience with dbt,
• exposure to graph databases, preferably Amazon Neptune (Neo4j, CosmosDB),
• solid knowledge of Git and CI/CD practice and Terraforn,
• experience with Apache Iceberg in production environments,
• knowledge of openLineage or similar lineage frameworks,
• experience with observability and monitoring frameworks for data platforms is a plus.

Recruitment Process:

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