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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:

• Owning and operating existing data and feature pipelines powering recommendation and advertising machine learning systems.
• Ensuring billions of daily events flow reliably through ingestion and processing, supported by monitoring, data-quality checks, and dependable backfills.
• Supporting Data Scientists and Analysts by providing reliable data for production models and experiments.
• Taking over in-flight initiatives and maintaining momentum on ongoing pipeline and platform improvements.
• Troubleshooting and resolving data issues to keep the platform stable and within SLA.
• Maintaining documentation and runbooks to ensure smooth knowledge transfer.

Your profile:

• Several years of hands-on Data Engineering experience, with the ability to become productive from day one and work independently.
• Strong programming skills in Python, SQL, and Spark , with proven experience building and operating large-scale ETL/ELT pipelines.
• Hands-on experience with an orchestration tool such as Airflow .
• Experience working with a major cloud provider, preferably GCP ; AWS or Azure experience is also welcome.
• Hands on experience with Kubernetes.
• Solid software engineering fundamentals, including Git, CI/CD, testing, and clean, maintainable code .
• Strong ownership mindset, reliability, and the ability to troubleshoot production issues independently.
• Clear communication skills and the ability to collaborate effectively with Data Scientists, Analysts, and Engineering teams.
• Working in hybrid (Stockholm) or fully remote model.

Nice to have:

• Experience with a feature store or production ML data pipelines.
• Hands-on experience with BigQuery .
• Exposure to recommendation systems, ranking, or advertising data .

Recruitment Process:

• CV review - HR call - Interview - Client Interview - Decision