Senior Azure Data Engineer

Role details

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 Millennium, ING, Play, Edenred, Arabian Drilling, FedEx, Leroy Merlin, Truecaller, Volotea, Schmitz Cargobull, 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: Mapping candidate-to-cash business processes to the underlying source systems. Profiling ATS, payroll, finance, HR and other relevant platforms to assess available data and its quality. Identifying data quality issues, integration challenges and data governance requirements. Defining data requirements for complex recruiter cost-allocation processes. Designing data ingestion and transformation pipelines using Azure Databricks and the Bronze, Silver and Gold layers. Preparing technical design documentation and engineering standards. Defining dbt project structures, testing approaches, naming conventions and CI/CD practices. Assessing the feasibility of requested KPIs by validating data availability and quality. Providing technical assumptions and sizing inputs for delivery-phase estimates. Identifying technical risks, dependencies and data-related constraints. Collaborating with architects, platform engineers, business stakeholders and the client’s technical teams. Participating in workshops, architecture reviews and design validation sessions. Your profile: Strong experience designing data platforms in Azure, with hands-on expertise in Azure Databricks. A solid understanding of Medallion Architecture and modern data lake architectures, including Delta Lake. Strong SQL and data modelling skills. Experience designing ingestion and transformation pipelines across multiple source systems. Background in source system analysis, data profiling and data quality assessment in large-scale enterprise environments. Experience defining data integration strategies across HR, payroll, finance or operational systems. Practical knowledge of dbt and data engineering best practices. Understanding of data governance, lineage, testing and quality controls. Experience documenting technical designs and engineering standards. Familiarity with Microsoft Fabric and Power BI ecosystems. The ability to estimate engineering effort and identify technical risks and dependencies. Strong communication, analytical and problem-solving skills. Confidence facilitating technical discussions and workshops with both technical and business stakeholders. Independence and a structured approach to work in a consulting environment. Excellent documentation skills and the ability to translate business requirements into technical solutions. Work from the European Union region and a work permit are required. Nice to have: Experience with Azure Data Factory, Unity Catalog or Microsoft Entra ID. Knowledge of data security and access segregation models. Experience implementing CI/CD for analytics platforms. Familiarity with recruitment, staffing, HR, payroll or finance datasets. Experience designing KPI and reporting data models. Involvement in data platform roadmaps or estimation activities. Previous consulting or client-facing experience, especially in Discovery & Design engagements. Experience presenting technical findings to technical and business audiences. Contributions to architecture reviews or strategic planning exercises. Recruitment Process: CV review – HR call – Interview – Client Interview – Decision