Data Engineer

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. 

About project:

We are looking for Data Engineers with strong Apache Airflow experience to join a project focused on customer enablement and engineering for a managed Airflow platform. The project supports data engineering teams in adopting, integrating, and operating Apache Airflow within their existing technology ecosystems.

This role is a great opportunity to combine hands-on Airflow engineering with technical consulting and customer enablement. You will help customers design and troubleshoot DAGs, integrate Airflow with their data platforms, and successfully transition workloads into stable production environments.

You will work closely with customer data engineering teams and an internal project owner, providing technical guidance while helping customers adopt best practices and resolve complex platform and production challenges.

You will:

• provide hands-on support with Apache Airflow configuration, DAG development, scheduling, and troubleshooting,
• advise customer data engineering teams on best practices for using Airflow across different technology stacks,
• help customers integrate Airflow with their existing data ecosystems, including cloud data warehouses, transformation tools, and Infrastructure as Code solutions,
• support customers during initial platform adoption and production onboarding,
• troubleshoot complex Airflow and distributed system issues and guide customers towards stable and reliable solutions,
• assist customers with operational challenges related to scheduling, dependencies, failures, and production workloads,
• deliver technical guidance, enablement, and knowledge sharing to customer engineering teams,