Assess and redesign multi-store reporting architecture; design scalable, high-performance analytics and real-time/near-real-time reporting solutions; define data modeling and event enrichment strategies; implement efficient unique-count and aggregation approaches; recommend technologies and trade-offs; support migration and ensure reporting consistency; drive data quality, observability, and performance optimizations; mentor and guide engineering teams.
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.
You will be:
- analyzing and assessing the current multi-store reporting architecture,
- designing a scalable, high-performance reporting solution capable of handling very large data volumes,
- defining data modeling strategies for analytics and reporting workloads,
- designing approaches for event enrichment using metadata from multiple sources,
- creating efficient solutions for computing unique counts and analytical aggregations at scale,
- providing architectural guidance around real-time and near real-time analytics,
- evaluating and recommending technologies, patterns, and trade-offs across the data platform,
- collaborating with engineering teams to implement the proposed architecture,
- supporting migration activities and ensure reporting consistency throughout the transition,
- driving best practices related to data quality, scalability, observability, and performance optimization,
- providing technical guidance and mentorship to engineering team members.
- 7+ years of experience in Data Engineering, Data Architecture, or related roles,
- strong hands-on expertise with Apache Druid,
- strong experience with Kafka and event-driven architectures,
- excellent knowledge of large-scale analytical data modeling,
- experience designing real-time or near real-time reporting platforms,
- strong expertise in SQL and MySQL,
- experience processing and analyzing billions of events,
- deep understanding of distributed data systems and performance optimization,
- ability to independently assess existing architectures and propose improvements,
- excellent communication skills and stakeholder management capabilities,
- experience with Apache Flink as well as streaming data pipelines and stream processing.
- fluent English (spoken and written).
Work from the European Union region and a work permit are required.
Nice to have:- knowledge of data enrichment and dimensional modeling techniques,
- experience implementing scalable unique-count solutions (e.g., HyperLogLog or similar approaches),
- exposure to cloud-based data platforms and modern analytics architectures,
- experience mentoring engineers and providing technical leadership,
- experience with ClickHouse,
- experience with modern lakehouse architectures, open table formats (e.g., Apache Iceberg), or data lakes,
- background in SaaS, martech, or customer engagement platforms.
CV review – HR call – Interview – Client Interview – Decision
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