Senior Azure Databricks Engineer(ID: 4002)

As a Senior Azure Databricks Engineer, you will: Design, develop, and maintain scalable and reliable data processing solutions using Azure Databricks. Build and manage robust batch and streaming data pipelines within Databricks environments. Develop and optimize data transformation and processing solutions using Python, PySpark, and SQL. Design and optimize data models to support scalable processing, performance, and reliability. Manage multiple parallel data processing workflows and shared data sources efficiently. Implement and maintain CI/CD pipelines using Azure DevOps and YAML-based configurations. Apply Infrastructure as Code (IaC) using ARM/Bicep for deployment and infrastructure automation. Monitor, troubleshoot, and optimize data processing workloads and Databricks environments. Collaborate with cross-functional engineering and business teams to deliver reliable and maintainable data solutions. Contribute to Agile development practices and continuously improve engineering standards, system stability, and performance. What You Bring to the Table: Strong hands-on experience with Azure Databricks as a core data engineering platform. Strong proficiency in Python, PySpark, and SQL. Hands-on experience developing batch and streaming data pipelines. Experience with data modeling, transformation, and optimization within Databricks environments. Good understanding of Azure cloud services relevant to data engineering. Experience with Azure DevOps, CI/CD, and YAML-based pipeline configurations. Hands-on experience with Infrastructure as Code, particularly ARM/Bicep. Experience working in Agile engineering and delivery environments. Understanding of modern cloud-based data architectures and end-to-end data engineering solutions. Strong communication, collaboration, troubleshooting, and problem-solving skills. You Should Possess the Ability to: Build scalable, high-performance, and reliable data processing solutions using Azure Databricks. Develop efficient PySpark and SQL-based data transformations. Design and manage complex batch and streaming workloads. Optimize data pipelines, processing performance, and resource utilization. Troubleshoot complex data engineering issues and improve system reliability. Implement automated deployment and infrastructure management practices. Make pragmatic technical decisions while maintaining scalability and maintainability. Work effectively with engineering, architecture, and business stakeholders. Drive continuous improvement and maintain high standards of code and solution quality. What We Bring to the Table: Opportunity to work on enterprise-scale Azure and Databricks data engineering initiatives. Exposure to modern cloud-based data platforms and engineering practices. A collaborative Agile environment focused on technical excellence and innovation. Opportunities to work with advanced data processing, pipeline engineering, and cloud technologies. Continuous learning and opportunities for technical and professional growth. A culture focused on quality, ownership, scalability, and sustainable engineering solutions.