Microsoft Fabric Data Engineer

Enterprise organizations face mounting pressure to unify, govern and activate data across complex platforms and business units. This is the Microsoft Fabric Data Engineer (m/f/d) role at EPAM in Germany, the Netherlands or the UK, working hybrid. You help clients build, deploy and run enterprise-grade data and analytics solutions using Microsoft Fabric and Power BI, collaborating both remotely and on-site. The role connects target architectures and design documents to production-ready solutions, automating deployments and ensuring every delivery meets engineering standards. You work closely with Solution Architects, client IT and data teams, and business users, raising risks early and contributing to high-quality, maintainable solutions. The work matters because trusted, accessible data drives real business outcomes and supports growth across regulated industries. Responsibilities 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 (SQL databases, APIs, files, SaaS applications, SAP, streaming sources) using pipelines, shortcuts and mirroring Write clean, reusable and tested code in PySpark, Spark SQL, T-SQL and KQL, optimizing 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 like DAX Studio, Tabular Editor, Best Practice Analyzer and Performance Analyzer Requirements 3+ years of hands-on experience in data engineering, BI development or data platform implementation 1+ year of hands-on Microsoft Fabric experience, with at least one delivered production or proof-of-concept project 2+ years of Power BI development experience: 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 (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 way of working and strong focus on quality English (B2+) and German (B1+) communication skills Nice to have Certifications: DP-700 (strongly preferred), DP-600, PL-300, DP-203 or Azure data engineering background Experience with Microsoft Purview (data catalog, lineage, sensitivity labels) Familiarity with Power Platform (Power Apps, Power Automate, Dataverse) and its integration with Fabric, such as translytical task flows or Dataverse links Knowledge of Semantic Link, Fabric REST APIs and community tools (Tabular Editor, DAX Studio, ALM Toolkit, Fabric CLI) Hands-on experience with Git, CI/CD and deployment pipelines for data and BI solutions Experience with Infrastructure as Code (Bicep/Terraform) and automated testing for data pipelines Awareness of AI and GenAI on data platforms: Copilot in Fabric, Fabric data agents, Azure AI Foundry, preparing data for retrieval-augmented generation scenarios Experience in migration projects from Synapse, Databricks, Snowflake, SSAS, Tableau or Qlik Experience in regulated industries such as finance, insurance, healthcare, energy, manufacturing or the public sector