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описание
The role helps enterprise organizations unify, govern, and activate data across complex platforms and business units. It delivers enterprise-grade data and analytics solutions using Microsoft Fabric and Power BI, connecting target architectures and design documents to production-ready solutions. The work supports trusted, accessible data and business outcomes across regulated industries.
задачи
Implement end-to-end Microsoft Fabric solutions based on approved architecture, including OneLake, Lakehouse, Warehouse, Data Factory pipelines, Dataflows Gen2, Notebooks (PySpark/Spark SQL), and Real-Time Intelligence
Build medallion architectures (Bronze/Silver/Gold), including incremental loads, change data capture, slowly changing dimensions, and error handling
Set up ingestion from diverse sources, including SQL databases, APIs, files, SaaS applications, SAP, and streaming sources, using pipelines, shortcuts, and mirroring
Write clean, reusable, and tested code in PySpark, Spark SQL, T-SQL, and KQL, and optimize Delta tables
Add data quality checks, validation rules, logging, and alerting to data pipelines
Build enterprise semantic models (star schema) using Direct Lake, Import, or DirectQuery as specified in the architecture
Write efficient DAX measures, calculation groups, and Power Query (M) transformations
Build reports and dashboards that follow customer design guidelines and best practices
Implement row-level and object-level security, sensitivity labels, and workspace permissions as defined in the security concept
Tune model and report performance using tools such as DAX Studio, Tabular Editor, Best Practice Analyzer, and Performance Analyzer
требования
3+ Years of hands-on experience in data engineering, BI development, or data platform implementation
1+ Year of hands-on Microsoft Fabric experience, including at least one delivered production or proof-of-concept project
2+ Years of Power BI development experience, including semantic modeling, advanced DAX, Power Query (M), Power BI Service administration, and performance tuning
Strong SQL (T-SQL) skills and good working knowledge of Python/PySpark and Spark SQL in notebooks
Hands-on experience with Azure data services such as Azure Data Factory, ADLS Gen2, Azure SQL, Synapse, or Databricks
Solid understanding of data warehousing and lakehouse concepts, Delta/Parquet formats, and dimensional modeling (Kimball)
Experience with Real-Time Intelligence (Eventstreams, Eventhouse/KQL, Activator)
Ability to turn architecture documents and requirements into working, well-documented solutions
Analytical mindset, structured approach to work, and strong focus on quality
English B2+ and German B1+ communication skills
Будет плюсом: DP-700 certification (strongly preferred), DP-600, PL-300, DP-203 or an Azure data engineering background, Microsoft Purview, Power Platform (Power Apps, Power Automate, Dataverse) and its integration with Fabric, Semantic Link, Fabric REST APIs, community tools (Tabular Editor, DAX Studio, ALM Toolkit, Fabric CLI), Git, CI/CD, deployment pipelines for data and BI solutions, Infrastructure as Code (Bicep/Terraform), automated testing for data pipelines, AI and GenAI on data platforms, migration projects from Synapse, Databricks, Snowflake, SSAS, Tableau, or Qlik, regulated industries such as finance, insurance, healthcare, energy, manufacturing, or the public sector
