Senior Data Designer

Location: Almaty, Kazakhstan; Astana, Kazakhstan; Belgrade, Serbia; Cluj-Napoca, Romania; Dnipro, Ukraine; Kharkiv, Ukraine; Krakow, Poland; Kyiv, Ukraine; Larnaca, Cyprus; Lodz, Poland; Lublin, Poland; Lviv, Ukraine; Odesa, Ukraine; Remote.BG, Bulgaria; Remote.GE, Georgia; Remote.KZ, Kazakhstan; Remote.PL, Poland; Riga, Latvia; Sofia, Bulgaria; Tbilisi, Georgia; Varna, Bulgaria; Warsaw, Poland; Wroclaw, Poland; Yerevan, Armenia We are looking for a Senior Data Designer who can operate as the bridge between business stakeholders and engineering — someone equally comfortable running a requirements workshop and designing a Kimball-style dimensional model. This is not a pure modelling role: you'll investigate source systems, challenge and shape business requirements before any design work starts, and turn that understanding into governed, well-documented data platform designs that engineering teams can implement. You will contribute to data ingestion design, transformation logic, and data harmonization while working closely with stakeholders and platform teams. This project focuses on building and enhancing an enterprise data warehouse platform that supports data driven decision making across complex business domains. The work involves designing scalable data models, ingestion pipelines, and transformation logic to ensure high quality and consistent data. Design and document Enterprise Data Warehouse objects: data models, source-to-target mappings, model diagrams, and supporting design documentation that engineers deliver from Work with stakeholders and source-system SMEs to onboard new data sources and adapt designs to what each source provides Clarify the intent behind requirements before designing: why the stakeholder needs it and whether existing structures already cover it Profile and analyze source data to validate design assumptions Define data quality rules as part of designs for downstream teams to implement Investigate reported data issues, identify root causes, and propose design-level fixes Take designs through peer and design review; give and take constructive challenge Keep stakeholders informed: raise issues early, ask instead of assuming, follow up until unblocked Strong practical foundations of dimensional modeling — able to design warehouse models from raw source data and explain the reasoning behind design choices Hands-on experience producing source-to-target mapping documentation Working understanding of data platform layering and data load approaches Confident SQL for data profiling and validation Strong written and spoken English; comfortable gathering requirements and managing stakeholder expectations Attention to detail; comfortable working in a governed environment with reviews and design standards, and data privacy/PII classification requirements SAP as a source system Retail domain experience Agile delivery experience