Senior Data Streaming 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.

We are looking for a Senior Data Streaming Engineer to join a project delivered for our international client, working with large-scale, event-driven systems and real-time data processing.

You will join a dedicated Streaming team responsible for building and operating production systems based on Kafka-compatible event streaming and Apache Flink. The role combines stream-processing development with the practical challenges of operating distributed systems, ensuring reliable event delivery, and managing stateful workloads in a cloud-native environment.

You will work hands-on across the streaming stack, contributing to production workloads running on AWS and Kubernetes, while developing services and components in Go.

• hands-on production experience with Kafka-compatible event-streaming technologies,
• practical understanding of Kafka concepts, including partitions, consumer groups, delivery guarantees, and schema evolution,
• experience developing stream-processing solutions with Apache Flink,
• understanding of Flink concepts, including event time, watermarks, state management, and checkpoints,
• practical experience developing services or components in Go,
• experience working with AWS infrastructure,
• hands-on Kubernetes experience, including deploying, operating, and troubleshooting workloads,
• strong problem-solving skills and the ability to work with distributed systems in production environments.
• 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.

• experience working with Kubernetes operators and Custom Resource Definitions (CRDs),
• experience operating stateful or distributed workloads on Kubernetes,
• experience troubleshooting performance, reliability, and scaling challenges in real-time data-processing systems.
• 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

• building and maintaining production-grade, Kafka-compatible event-streaming solutions,
• managing partitions, consumer groups, delivery guarantees, and schema evolution,
• developing and maintaining stream-processing applications using Apache Flink,
• working with event time, watermarks, state management, and checkpoints,
• developing services and components in Go,
• deploying and operating streaming workloads on AWS-based infrastructure,
• running and troubleshooting workloads on Kubernetes,
• contributing to the reliability, scalability, and operational stability of distributed streaming systems,
• investigating production issues and improving the performance and resilience of streaming applications,
• collaborating with other engineers to evolve the streaming platform and follow engineering best practices.