Data & AI Engineer

• Design, build, and maintain data ingestion pipelines from SQL Server into Azure Databricks using CDC-based and batch patterns
• Implement and manage medallion architecture (Bronze, Silver, Gold) using Delta Lake and Unity Catalog
• Write and optimize complex T-SQL queries, stored procedures, and CDC configurations on Microsoft SQL Server
• Develop PySpark and Spark SQL transformations for large-scale data processing and curated analytical layers
• Build and maintain vector database pipelines that transform structured and unstructured source data into embeddings for downstream AI and search applications
• Collaborate with BI and analytics teams to deliver curated data models, dashboards, and AI/BI Genie experiences
• Configure and troubleshoot connectivity between SQL Server, Databricks, and third-party connector applications
• Monitor pipeline health, implement alerting, and resolve data quality and performance issues proactively
• Maintain technical documentation for pipelines, schemas, and architectural decisions

• 3+ years of hands-on experience with Microsoft SQL Server (2016 or later)
• Proficiency in T-SQL including CTEs, window functions, dynamic SQL, and query optimization
• Experience configuring and managing Change Data Capture (CDC) for incremental data extraction
• Ability to read execution plans; experience with indexing strategies and statistics management
• Working knowledge of isolation levels, locking behavior, and blocking resolution (RCSI, snapshot isolation)
• Understanding of replication topologies and their impact on downstream pipeline design

• 2+ years building notebooks, jobs, and workflows in Azure Databricks
• Hands-on experience implementing Bronze/Silver/Gold medallion architecture using Delta Lake
• Proficiency in PySpark and Spark SQL for large-scale data transformation
• Experience with Unity Catalog for governance and access control
• Experience implementing CDC-based incremental pipelines using watermarks or Delta merge patterns
• Familiarity with cluster configuration, compute management, and job scheduling

• Experience configuring JDBC/ODBC connectivity between SQL Server and cloud compute platforms
• Familiarity with one or more connector/orchestration platforms (Azure Data Factory, Fivetran, dbt, or similar)
• Understanding of Azure networking (VNet peering, NSGs, private endpoints)
• Experience with secret management using Azure Key Vault and Databricks secret scopes

• Experience designing and managing vector databases (Pinecone, Weaviate, Chroma, pgvector, or similar)
• Understanding of embedding models and how to generate, store, and query vector representations of data
• Familiarity with similarity search concepts (cosine similarity, ANN indexing such as HNSW or IVF)
• Experience integrating vector stores into retrieval pipelines (RAG patterns, semantic search, or recommendation systems)

• Proficiency in Python for pipeline development and automation
• Version control experience using Git
• Strong written communication skills; ability to produce and maintain technical documentation
• Comfortable working within Azure cloud environments

• Experience with Tableau, Power BI, or similar BI tools as a downstream data consumer
• Familiarity with ERP source systems (field service management, inventory, or financial platforms)
• Experience with dbt for transformation layer management
• Knowledge of cloud cost optimization strategies for Databricks compute
• Microsoft Certified: Azure Data Engineer Associate or equivalent certification
• Exposure to LLM orchestration frameworks such as LangChain or LlamaIndex

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