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 to join a project where our client is building a new data mesh platform on AWS. Until now, central data teams owned the data. The new model asks product engineering teams to own the data they produce and consume, which is as much a cultural shift as a technical one.

You will join a lean, fast-moving platform team of around six engineers with a part-time Product Manager. Your job will be to make that shift real: build the first wave of data products on the new platform, onboard data out of the legacy warehouses, and work side by side with product engineering teams so they can design, own, and run their own data.

This is a hands-on engineering role with a strong enablement component. Expect to spend a meaningful part of your week working with other teams, not only in your own editor.

You will:

• design and build consumable data assets (CDAs, also known as data products) on the client's AWS data platform, including defined schemas, proper documentation, and clear ownership, then hand them over to the owning product team,
• work with product engineering teams and platform users to agree data contracts, map domain ownership, and understand how their data fits into the wider landscape,
• onboard bronze and silver layer data from existing data warehouses using the client's internal agentic tooling,
• build new tooling that speeds up adoption of the data platform,
• build streaming and batch pipelines using Flink, SQL, Python, Kafka, Iceberg, and DynamoDB on AWS,
• help engineering teams debug pipeline and batch job issues,
• promote engineering best practices across the platform through practical guidance and collaboration,