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Azure Databricks Solutions Architect
1 week ago
**Birlasoft Overview**
Birlasoft is a powerhouse where domain expertise, enterprise solutions, and digital technologies converge to redefine business processes.
We take pride in our consultative and design thinking approach, driving societal progress by enabling our customers to run businesses with unmatched efficiency and innovation.
As part of the CK Birla Group, a multibillion-dollar enterprise, we boast a 12,500+ professional team committed to upholding the Group's 162-year legacy.
We prioritize Diversity, Equity, and Inclusion (DEI) initiatives, along with Corporate Sustainable Responsibility (CSR) activities, demonstrating our dedication to building inclusive and sustainable communities.
About the Role - Azure Databricks Platform Lead EngineerThe successful candidate will design and maintain data pipelines using Azure Data Factory and Databricks, ensuring smooth and efficient data flow through the pipelines.
You will troubleshoot and optimize existing data pipelines for performance and reliability, and implement measures to ensure data quality and integrity across various data sources.
In addition, you will work closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver solutions, providing processed and usable data for business decision-making.
As a Cloud Data Engineer, you will implement data security best practices, including encryption and role-based access control (RBAC), and ensure compliance with data governance and regulatory requirements.
You will also use tools like Azure Monitor to track data pipeline performance and troubleshoot issues, and manage and monitor cluster performance to avoid overload and service throttling.
- **Responsibilities:**
- Design and Maintain Data Pipelines: Develop and optimize data pipelines using Azure Data Factory and Databricks.
- Troubleshooting and Optimization: Identify and resolve issues related to cluster creation, performance, and resource allocation.
- Data Quality and Integrity: Implement measures to ensure data quality and integrity across various data sources.
- Collaboration and Communication: Work closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver solutions.
- Security and Compliance: Implement data security best practices, including encryption and role-based access control (RBAC).
- Monitoring and Performance Management: Use tools like Azure Monitor to track data pipeline performance and troubleshoot issues.
