Senior Streaming 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. 

We are looking for Senior Data Engineers to join a project delivered for our international client operating in a data-intensive and technology-driven environment. The client is building modern real-time data platforms that power operational and near real-time business use cases such as logistics, tracking, fraud detection, customer interactions, and intelligent decision-making systems.

This role is a great opportunity to work on large-scale event-driven architectures and streaming platforms processing high volumes of data with strict latency and reliability requirements. You will contribute to the design, development, and optimization of real-time data pipelines and distributed processing systems that enable business-critical applications and analytics capabilities.

The project combines data engineering, distributed systems, cloud technologies, and streaming architectures within a collaborative international environment. You will work closely with platform engineers, machine learning teams, product stakeholders, and other engineering disciplines to deliver scalable and reliable real-time data solutions.

• designing and implement real-time data pipelines and streaming architectures,
• building, maintaining, and improving event-driven data processing systems,
• ensuring low-latency and high-throughput processing of streaming data at scale,
• integrating multiple internal and external data sources into streaming pipelines,
• implementing monitoring, alerting, logging, and observability solutions for real-time systems,
• ensuring data quality, consistency, and reliability within streaming environments,
• collaborating closely with platform, machine learning, and product teams on real-time business use cases,
• optimizing pipeline performance, scalability, and operational stability,
• participating in architecture discussions related to distributed systems and event-driven platforms,
• supporting troubleshooting, debugging, and continuous improvement activities across data processing environments,
• contributing to engineering best practices, automation, and operational excellence initiatives,
• depending on seniority, take ownership of technical areas, support architectural decisions, and mentor less experienced team members,

• 5+ years of commercial experience in Data Engineering or related disciplines,
• strong programming skills in Python, Java, or Scala,
• hands-on experience with streaming technologies such as Kafka, Kinesis, Google Pub/Sub, or similar platforms,
• proven experience building real-time or near real-time data pipelines,
• strong understanding of distributed systems and event-driven architecture principles,
• practical experience working with AWS cloud services,
• experience with modern data processing frameworks such as Apache Flink, Spark Streaming, or similar technologies,
• strong understanding of scalability, fault tolerance, and reliability in distributed environments,
• experience working with modern software engineering practices, testing approaches, and CI/CD workflows,
• strong problem-solving, communication, and collaboration skills,
• ability to work effectively in cross-functional and international teams,
• experience with stateful stream processing and windowing concepts,
• understanding of exactly-once and at-least-once processing semantics,
• familiarity with monitoring, observability, and operational tooling for streaming platforms,
• experience integrating streaming pipelines with downstream analytics, reporting, or machine learning solutions,
• exposure to containerization and orchestration technologies such as Docker and Kubernetes,

• 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 with unified batch and streaming platforms such as Databricks, Apache Beam, or similar technologies,
• knowledge of real-time machine learning feature pipelines and feature engineering workflows,
• experience working with high-scale event-driven systems processing millions of events per day,
• experience contributing to platform-level architecture and technical strategy,
• familiarity with data platform modernization and cloud-native architecture patterns,

• 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.

CV review – HR call – Interview – Client Interview – Decision