Data Business Analyst (Kraków)

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 complex datasets to identify trends, patterns, and actionable insights,
• writing and optimizing complex SQL queries to support data analysis and product needs,
• ensuring data quality through validation, testing, and defining quality checks,
• profiling and exploring new and existing data sources to assess structure, usability, and reliability,
• conducting deep data investigations to understand lineage, inconsistencies, and anomalies,
• documenting data flows, business logic, constraints, and user processes to build organizational knowledge,
• collaborating closely with Data Engineering, Product, and business stakeholders on data-related initiatives,
• contributing to the development of data models (logical & physical), including semantic layers and curated datasets,
• recommending improvements to data structures for better performance, scalability, and usability,
• building and maintaining business and technical metadata (e.g. glossary, KPIs, data definitions),
• supporting the development and enrichment of the data catalogue across internal and external sources,
• assisting Product Managers in delivering outcomes within the product area,
• supporting business teams during UAT and post-production with analysis and data-related queries,
• partnering with engineering teams to inform system and data design decisions.

Your profile:

• ready to work from the client’s office in Krakow (3 days per week),
• strong experience working with data-heavy projects, preferably in retail or e-commerce environments,
• advanced SQL skills, including writing and optimizing complex queries,
• proven ability to analyze large, complex datasets and translate them into insights,
• experience with data validation, data quality checks, and ensuring data integrity,
• strong understanding of data discovery, data profiling, and source analysis,
• experience documenting data flows, logic, and business processes,
• familiarity with data modelling concepts (logical & physical models, semantic layers),
• ability to collaborate effectively with both technical (engineering) and business teams,
• detail-oriented mindset with a strong focus on data quality and consistency,
• upper intermediate/Advanced English (B2/C1),
• practical experience using AI-powered assistants (e.g. ChatGPT, Claude, Copilot Chat, or similar) or improve productivity, quality, or decision-making in analysis and software delivery.

Work from Poland and a work permit are required.

Nice to have:

• experience working with data catalogues and metadata management,
• exposure to data governance or data quality frameworks,
• experience supporting UAT processes and working closely with business stakeholders,
• background in large-scale data platforms or transformation initiatives,
• experience in the retail domain is a strong advantage,
• experience applying GenAI in a more structured way within the SDLC, including defined workflows, prompt patterns, or tool integrations embedded into daily analytical and dokumentation work,
• interest in and familiarity with emerging AI-driven practices (e.g. automation of analysis tasks, AI-supported documentation, or workflow optimization) with a willingness to explore and experiment beyond standard approaches.

Recruitment Process:

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