Remote - Senior Microsoft Fabric Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Remote Senior Microsoft Fabric Data Engineer for a long-term contract, with pay rates dependent on experience. Requires 8+ years in data warehousing and strong skills in Microsoft Fabric, SQL, PySpark, and data modeling.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
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🗓️ - Date discovered
July 29, 2025
🕒 - Project duration
Unknown
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🏝️ - Location type
Remote
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📄 - Contract type
Unknown
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🔒 - Security clearance
Unknown
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📍 - Location detailed
United States
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🧠 - Skills detailed
#Dataflow #Azure Databricks #Version Control #Big Data #BI (Business Intelligence) #Azure #Data Governance #Data Modeling #Spark (Apache Spark) #Synapse #Databricks #Microsoft Power BI #"ETL (Extract #Transform #Load)" #Azure Data Factory #Data Engineering #PySpark #GIT #Documentation #DAX #Azure Synapse Analytics #Data Warehouse #KQL (Kusto Query Language) #ADF (Azure Data Factory) #Data Integration #SQL (Structured Query Language)
Role description
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Role: Senior MS Fabric Engineer
Location: Remote
Duration: Long term
Rates: DOE
Prefer USC/GC (client is not sponsering for this opportunity)
Experience:
• 8+ years of hands-on experience in designing, implementing, and supporting data warehousing and business intelligence solutions, with a strong focus on Microsoft Fabric or similar tools within the Microsoft data stack (e.g., Azure Synapse Analytics, Azure Data Factory, Azure Databricks).
• Strong proficiency with Microsoft Fabric components:
• Data Engineering (Spark Notebooks, Spark Job Definitions)
• Data Factory (Dataflows Gen2, Pipelines)
• Data Warehouse / SQL Analytics Endpoints
• OneLake and Lakehouse architecture
• Power BI integration and DAX
• Solid understanding of data modeling (dimensional modeling, star schemas), ETL/ELT processes, and data integration patterns.
• Proficiency in SQL, PySpark, and/or T-SQL.
• Familiarity with KQL (Kusto Query Language) is a plus.
• Experience with big data technologies like Spark.
• Strong understanding of data and analytics concepts, including data governance, data warehousing, and structured/unstructured data.
• Knowledge of software development best practices (e.g., code modularity, documentation, version control - Git).
