Data Engineer

• Design and implement scalable batch and streaming data pipelines using Azure Databricks, Delta Live Tables, and Apache Spark.
  
• Build and maintain ETL/ELT workflows orchestrated through Azure Data Factory and Databricks Workflows.
  
• Develop reusable and modular pipeline components following software engineering best practices.
  
• Architect and manage Lakehouse solutions using Delta Lake across Bronze, Silver, and Gold layers.
  
• Design and enforce data models, schemas, and governance policies using Unity Catalog.
  
• Optimize storage, partitioning, and query performance for large-scale datasets on ADLS Gen2.
  
• Manage Databricks clusters, compute policies, and job scheduling.
  
• Implement Infrastructure as Code (IaC) using Terraform or ARM templates.
  
• Integrate Databricks with Azure services including Synapse, Event Hubs, Key Vault, and Azure DevOps.
  

Requirements

• Strong proficiency in PySpark, Python, and SQL for Big Data processing.
  
• Hands-on experience with Delta Lake, Delta Live Tables (DLT), and Medallion Architecture.
  
• Strong experience with Azure Data Services including:
  
• Azure Data Lake Storage Gen2 (ADLS Gen2)
  
• Azure Data Factory (ADF)
  
• Azure Synapse Analytics
  
• Azure Event Hubs
  
• Experience with Databricks Unity Catalog for data governance and access control.
  
• Experience implementing CI/CD pipelines using Azure DevOps or GitHub Actions for Databricks deployments.
  
• Strong understanding of distributed computing concepts, Spark optimization, partitioning, and performance tuning.
  
• Experience with streaming data processing using Structured Streaming, Kafka, or Event Hubs.