Azure Data engineer Walk In Drive at Noida
Tata Consultancy Services · Noida, India ·
- Category
- Data engineering
- Company size
- 1000+
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Azure Data Engineer role at TCS Noida involving data ingestion, transformation, storage, warehousing, and modeling using Azure cloud services like Data Factory, Databricks, Synapse Analytics, and Python/PySpark.
TCS has been a great pioneer in feeding the fire of Young Techies like you. We are a global leader in the technology arena and there's nothing that can stop us from growing together.
TCS is Hiring for Azure Data Engineer
Experience Range: - 6+
Job Locations : Noida
Desired Competencies (Technical/Behavioral Competency)
Must-Have**
(Ideally should not be
more than 3-5) • Strong proficiency in Python, Pyspark. Data Ingestion and Integration: Ingest data from various sources such as databases, files, APIs, and streaming platforms into Azure. For example, you could use Azure Data Factory to orchestrate the extraction, transformation, and loading (ETL) of data from an on-premises SQL Server database to Azure Blob Storage. • Data Transformation and Processing: Perform data transformation and processing tasks to prepare the data for analysis and reporting. Azure Databricks or Azure Synapse Analytics can be used for large-scale data transformations using tools like Apache Spark. • Data Storage and Management: Select and manage the appropriate storage solutions for your data. For instance, you could use Azure SQL Database or Azure Cosmos DB for structured data, Azure Data Lake Storage for big data and unstructured data, or Azure Blob Storage for file storage. • Data Warehousing: Implementing data warehousing solutions is a common use case for Azure Data Engineers. Azure Synapse Analytics (formerly Azure SQL Data Warehouse) offers scalable and distributed analytics capabilities for handling large datasets. • Data Modeling and Analysis: Collaborate with data scientists and analysts to design and implement data models for analytical workloads. Azure Analysis Services or Azure Databricks can be used for building and deploying models to support interactive data analysis.
Good-to-Have • DevOps and Automation: Automating data pipelines, monitoring data processes, and managing deployments are important tasks for Azure Data Engineers. They may use tools like Azure DevOps, Azure Monitor, and Azure Automation to achieve continuous integration and delivery.
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