Azure Data Engineer

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 Modelling 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.