Senior Microsoft Fabric Data Engineer

Client of OUTSOURCY

We are looking for Senior Microsoft Fabric Data Engineer to join our data & analytics consulting team .

: Hybrid (Cairo, Egypt)
: 5 7 years
This role is designed for an experienced data professional who can architect, build, and optimize complex data pipelines and end-to-end workflows in Microsoft Fabric, with a strong foundation in Azure Synapse, Azure Data Factory and enterprise data warehousing principles.


Design and Architect End-to-End Fabric Solutions Lead architecture and implementation across Fabric components
Data Pipelines, Lakehouses, Dataflows Gen2, Warehouses, and Semantic Models Develop and Optimize Data Pipelines Design, orchestrate, and monitor complex workflows in Microsoft Fabric, Azure Data Factory (ADF), and Azure Synapse Analytics Data Modeling & Transformation:
Apply advanced SQL and data modeling techniques (star schema, snowflake, normalization, SCD, CDC, etc.) to build scalable, maintainable data models.
Implement best practices in ELT/ETL, data ingestion, transformation, partitioning, and performance tuning Leverage Delta/Parquet formats, incremental loading, and metadata-driven ingestion patterns.
Work closely with Data Architects, Business Analysts, and BI Developers to align technical design with business goals.


Microsoft Fabric: Deep hands-on experience with Data Pipelines, Lake houses, Warehouses, Dataflows Gen2, and OneLake integration.
Azure Data Factory: Mastery in building, orchestrating, and monitoring large-scale pipelines.
Azure Synapse Analytics: Proficiency in dedicated SQL pools, server less queries, partitioning, and performance optimization.
SQL Mastery: Advanced SQL development (CTEs, window functions, dynamic SQL, tuning, indexing).
Data Lake Architecture: Delta Lake, Parquet, partitioning strategies, medallion (Bronze/Silver/Gold) design
Data Engineering Concepts: (ELT vs ETL, Incremental and full-load design, Orchestration and monitoring, Data quality and validation frameworks, Parallelism and pipeline optimization)
Data Warehousing Concepts: (Star/Snowflake schema design, SCDs, Data Modelling,
Data governance and security layers (Row-Level Security, sensitivity labels) Performance Optimization: Analyze workloads and optimize pipelines, partitioning, caching, and cost-performance balance.
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