Wall Street Consulting Services LLC
Azure Data Engineer with AI Fabric Experience
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an Azure Data Engineer with AI Fabric experience, located in Warren, NJ, for over 6 months. Key skills include Microsoft Fabric, PySpark, SQL, Azure Event Hubs, and production LLM experience.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
May 23, 2026
🕒 - Duration
More than 6 months
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🏝️ - Location
On-site
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Warren, NJ
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🧠 - Skills detailed
#Data Engineering #PySpark #Delta Lake #Azure Event Hubs #API (Application Programming Interface) #ADLS (Azure Data Lake Storage) #Azure Active Directory #Data Vault #BI (Business Intelligence) #Python #Microsoft Power BI #Data Modeling #Spark (Apache Spark) #Azure #JSON (JavaScript Object Notation) #AI (Artificial Intelligence) #GIT #S3 (Amazon Simple Storage Service) #SQL (Structured Query Language) #Vault
Role description
Job Title: Azure Data engineer with AI fabric experience
Location: Warren NJ
Duration: Long term
Job Description:
1.Microsoft Fabric & lakehouse engineering Hands-on production experience with Microsoft Fabric One Lake, Lakehouse, Data Factory pipelines, Spark notebooks, and Direct Lake mode for Power BI. Must have built and operated something real on Fabric Fluent in PySpark and Delta Lake: MERGE, schema evolution, time travel, OPTIMIZE, partitioning. Has built incremental ingestion from ADLS Gen2 or S3 into curated Delta tables. Strong SQL (window functions, CTEs, query optimization) and a working knowledge of Parquet internals, partitioning, predicate pushdown, and compaction.
2.Event-driven ingestion on Azure Has built production ingestion using Azure Event Hubs
3.Python engineering & Azure platform fundamentals Modular Python code with type hints, unit tests, and packaging. Git-based workflow with pull requests and CI. Working knowledge of ADLS Gen2, Azure Active Directory, Key Vault, and Managed Identity permissions and the engineer need to handle them confidently from day one.
4.Source-to-target data modeling with a canonical layer Has built or contributed to a canonical model EDP, data vault, or dimensional that decouples source systems from downstream consumers. Understands medallion architecture (Bronze / Silver / Gold) and can explain why each layer exists and what belongs in it.
1. Production LLM & embedding experience using an LLM API (Azure OpenAI strongly preferred for our stack) with structured output via JSON mode or function calling, paired with an embedding model for semantic search or matching.
Job Title: Azure Data engineer with AI fabric experience
Location: Warren NJ
Duration: Long term
Job Description:
1.Microsoft Fabric & lakehouse engineering Hands-on production experience with Microsoft Fabric One Lake, Lakehouse, Data Factory pipelines, Spark notebooks, and Direct Lake mode for Power BI. Must have built and operated something real on Fabric Fluent in PySpark and Delta Lake: MERGE, schema evolution, time travel, OPTIMIZE, partitioning. Has built incremental ingestion from ADLS Gen2 or S3 into curated Delta tables. Strong SQL (window functions, CTEs, query optimization) and a working knowledge of Parquet internals, partitioning, predicate pushdown, and compaction.
2.Event-driven ingestion on Azure Has built production ingestion using Azure Event Hubs
3.Python engineering & Azure platform fundamentals Modular Python code with type hints, unit tests, and packaging. Git-based workflow with pull requests and CI. Working knowledge of ADLS Gen2, Azure Active Directory, Key Vault, and Managed Identity permissions and the engineer need to handle them confidently from day one.
4.Source-to-target data modeling with a canonical layer Has built or contributed to a canonical model EDP, data vault, or dimensional that decouples source systems from downstream consumers. Understands medallion architecture (Bronze / Silver / Gold) and can explain why each layer exists and what belongs in it.
1. Production LLM & embedding experience using an LLM API (Azure OpenAI strongly preferred for our stack) with structured output via JSON mode or function calling, paired with an embedding model for semantic search or matching.
