Wall Street Consulting Services LLC

Azure Data Fabric Architect

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an Azure Data Fabric Architect in Warren, NJ, onsite for a 15+ month contract at a competitive pay rate. Key skills include Microsoft Fabric, PySpark, SQL, and experience in insurance data engineering and production LLM.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
June 30, 2026
🕒 - Duration
Unknown
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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
#Spark (Apache Spark) #Delta Lake #Data Modeling #Data Engineering #GIT #Microsoft Power BI #BI (Business Intelligence) #JSON (JavaScript Object Notation) #Python #Data Vault #PySpark #ADLS (Azure Data Lake Storage) #API (Application Programming Interface) #Azure #SQL (Structured Query Language) #Azure Active Directory #Vault #S3 (Amazon Simple Storage Service) #AI (Artificial Intelligence)
Role description
Job Title: Azure Data Engineer with AI Fabric Location: Warren, NJ (Onsite) Domain: Insuranc eExperience: 15 • + 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 compactio • n.Event-driven ingestion on Azure Has built production ingestion using Azure Event Hu • bsPython 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 on • e.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 i • t.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 matchin g.