Microsoft Cloud Data Engineer Crisil

About Crisil

Job Description

The Microsoft Cloud Data Engineer role is a great opportunity for a talented and motivated individual to design, construct, and manage cloud-based data solutions using Microsoft Azure technologies. Your primary responsibility will be to create strong, scalable, and secure data pipelines and support analytics workloads that drive business insights and data-based decision-making. You will design and deploy ETL/ELT pipelines using Azure Data Factory, Azure Synapse Analytics, Azure Databricks, and Azure Data Lake Storage. Additionally, you will be responsible for developing and overseeing data integration workflows to bring in data from various sources such as APIs, on-prem systems, and cloud services. It will also be important to optimize and maintain SQL-based data models, views, and stored procedures in Azure SQL, SQL MI, or Synapse SQL Pools. Collaboration with analysts, data scientists, and business teams will be crucial to gather data requirements and provide reliable and high-quality datasets. You will need to ensure data quality, governance, and security by implementing robust validation, monitoring, and encryption mechanisms. Supporting infrastructure automation using Azure DevOps, ARM templates, or Terraform for resource provisioning and deployment will also be part of your responsibilities. You will also play a role in troubleshooting, performance tuning, and the continuous improvement of the data platform. To qualify for this position, you should have a Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field. A minimum of 3 years of experience in data engineering with a focus on Microsoft Azure data services is required. Hands-on experience with Azure Data Factory, Azure Synapse Analytics, and Azure Data Lake is a must. Strong proficiency in SQL and data modeling is essential, along with experience in Python, PySpark, or .NET for data processing. Understanding of data warehousing, data lakes, and ETL/ELT best practices is important, as well as familiarity with DevOps tools and practices in an Azure environment. Knowledge of Power BI or similar visualization tools is also beneficial. Additionally, holding the Microsoft Certified: Azure Data Engineer Associate certification or its equivalent is preferred.,