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.
IIn the CEE region, we’re a team of nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for clients such as Millennium, ING, Play, Edenred, Arabian Drilling, FedEx, Leroy Merlin, Truecaller, Volotea, Schmitz Cargobull, 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:
- developing and maintaining ingestion pipelines handling billions of events per day,
- designing, building and optimising complex ETL pipelines,
- developing the tools and frameworks that make up the data platform,
- supporting other teams working on and with the platform,
- working with Data Scientists to take ML models into production,
- contributing to the ML platform and to streaming solutions,
- setting good practice across software and data engineering,
- identify bottlenecks in the existing platform and improving them.
Your profile:
- 5+ years of experience as a Data Engineer or in a comparable role,
- strong Spark, Scala and Python skills,
- hands-on experience with Google Cloud Platform,
- a track record of building complex ETL pipelines,
- experience working with large scale data sets,
- knowledge of Airflow or a comparable orchestration tool,
- experience with Git and CI/CD practices and tools, such as Jenkins,
- solid software engineering fundamentals,
- autonomy, ownership and clear communication in English,
- open to work on-site in Stockholm.
Nice to have:
- experience with Kafka or other messaging systems,
- experience with streaming data and kubernetes,
- knowledge of BigQuery or Redshift
- previous work on an ML platform or a data platform.
Recruitment Process:
CV review – HR call – Interview – Client Interview – Decision
