Azure Data Engineer + AI

Key Responsibilities

• Data Pipeline & Architecture: Design and build robust ETL/ELT data pipelines using Azure Data Factory, Azure Databricks, and Microsoft Fabric to process batch and real-time streaming data. [1, 2]
• AI & LLM Data Integration: Prepare, clean, and structure enterprise data lakes/lakehouses to feed Azure OpenAI, vector databases, and Retrieval-Augmented Generation (RAG) architectures. [1]
• Data Modeling & Governance: Implement medallion architectures (Bronze, Silver, Gold), ensure data quality, and manage data governance/lineage using tools like Microsoft Purview or Unity Catalog. [1, 2]
• Collaboration: Partner with data scientists, AI engineers, and business analysts to operationalize machine learning models and AI agents. [1]

Required Skills & Qualifications

• Cloud Platforms: Hands-on experience with Microsoft Azure (ADF, Azure Databricks, Azure Synapse Analytics, ADLS Gen2, and Microsoft Fabric).
• Programming & Querying: Advanced proficiency in Python, PySpark, Spark SQL, and complex SQL.
• AI & GenAI Familiarity: Knowledge of vector embeddings, semantic search, prompt grounding, and integrating Azure AI / OpenAI endpoints.