Senior 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, act as a bridge between teams and divisions, helping raise the data engineering bar through pairing, reviews, and hands-on guidance rather than top-down mandates, take ownership of technical initiatives and drive them through to successful completion,
• Your profile: solid data engineering background built on large-scale data platforms and big data workloads, hands-on experience with stream processing, event-driven architecture, and schema management, strong streaming experience combined with practical batch data engineering experience, working knowledge of AWS as a user of an established platform, with enough experience to stand up streaming pipelines and batch jobs, practical experience working with Kafka, Iceberg, DynamoDB, and core AWS services, exposure to Apache Flink using SQL or Python (PyFlink), with the ability to become productive within weeks rather than requiring deep specialization, strong stakeholder management and communication skills, proactive approach to working with other engineering teams and platform users, ability to explain the value of data ownership and move technical discussions from hesitation to agreement, experience at Lead Engineer level, including setting technical direction, running work end to end, and owning outcomes rather than individual tickets, fluent English, both written and spoken, practical experience using AI-powered assistants (e.g. Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making in software delivery.
• Work from the European Union region and a work permit are required.
• Nice to have: practical experience with AI-assisted engineering and agentic tooling applied to data onboarding, tooling, and pipeline development, exposure to data mesh concepts, including data products, data contracts, domain ownership, and federated governance, awareness of Kubernetes at a level that allows you to understand how the platform runs, experience in a data platform enablement or internal developer experience role, background in high-volume operational or transactional environments where both data volume and latency matter, experience applying GenAI in a more structured way within the SDLC, including defined workflows, prompt patterns, or tool integrations embedded into daily work.
• Interest in and familiarity with emerging AI-driven practices (e.g. agent-based workflows, automation patterns, AI-augmented development), with a willingness to explore and experiment beyond standard approaches.
• Recruitment Process: CV review –
• HR call –
• Interview
• –
• Client
• Interview –
• Decision