Data & Analytics Consultant (Power BI)

Data & Analytics Consultant (Power BI)

Responsibilities

• Designing and implementing scalable data platforms, data warehouse, and analytics solutions for clients and internal products—covering everything from data integration to delivery for reporting, BI, and advanced analytics.
• Designing technology-agnostic target architectures for data warehouses, data lakes, and modern data platforms, and translating business requirements into robust technical concepts.
• Developing and optimizing data pipelines using modern data pipelining and ETL/ELT technologies (e.g., Microsoft Fabric, Azure Data Factory, SSIS) while ensuring data quality, performance, and operational reliability.
• Analysing client requirements and providing advice on data strategies, integration scenarios, and analytics use cases—from initial concept to production-ready solution.
• Collaborating closely within interdisciplinary teams, actively sharing knowledge, and contributing to the further development of the data & analytics consulting portfolio.
• Fostering a data culture by communicating the value of data-driven decisions and contributing to the continuous modernization of clients‘ analytics landscapes.

Profile / Skill Requirements

• Degree in Computer Science, Business Informatics, or a comparable qualification, plus at least three years of practical experience in the data & analytics field (ideally in consulting or software engineering).
• Solid experience in data integration and building data warehouse, data lake, or data platform solutions, combined with a strong interest in making data optimally usable for analytics and reporting.
• Experience designing and implementing data pipelines; ideally, familiarity with tools such as Microsoft Fabric, Azure Data Factory, and SSIS.
• Proficiency in software development with Python, as well as experience using build and version control tools and setting up CI/CD pipelines (e.g., with Git).
• Strong communication and collaboration skills; ability to communicate effectively with diverse stakeholders and explain complex technical concepts in an understandable way.
• Knowledge of common data modeling methods (e.g., dimensional models) as well as experience with the Azure cloud platform in the context of data and analytics solutions.