Azure Data Engineer

2 months ago


Bangalore, India Kiash Solutions LLp Full time

Job Description :


Skills Required :


- Azure : Strong experience working with Microsoft Azure cloud platform, including services such as Azure Data Lake Storage, Azure SQL Database, Azure Synapse Analytics, Azure Data Factory, Azure Databricks, and Azure HDInsight.


- ETL Tools : Proficiency in designing, developing, and maintaining ETL (Extract, Transform, Load) processes using Azure Data Factory or similar ETL tools to ingest and process data from various sources into Azure data services.


- Azure Databricks : Experience in using Azure Databricks for big data processing and analytics tasks, including data exploration, data engineering, and machine learning workflows. Familiarity with Databricks notebooks and Spark jobs optimization.


- PySpark : Strong expertise in PySpark, the Python API for Apache Spark, for distributed data processing and analytics. Ability to write efficient PySpark code for data transformations, aggregations, and analytics tasks.


- Power BI (Good to have): Familiarity with Microsoft Power BI for data visualization and reporting. Ability to create interactive dashboards, reports, and data visualizations to provide insights into the data processed and stored in Azure.


Responsibilities :


- Data Engineering : Design and implement end-to-end data engineering solutions on Azure, including data ingestion, transformation, and loading processes using Azure Data Factory, Azure Databricks, and other Azure data services.


- ETL Development : Develop and optimize ETL pipelines to extract data from various sources, transform it according to business requirements, and load it into Azure data stores such as Azure Data Lake Storage and Azure SQL Database.


- Data Processing : Utilize Azure Databricks and PySpark for distributed data processing tasks such as data cleansing, enrichment, aggregation, and analysis on large-scale datasets stored in Azure.


- Data Modeling : Design and implement data models and schemas to support data analytics and reporting requirements. Optimize data structures for efficient querying and analytics performance.


- Performance Optimization : Optimize PySpark jobs and Databricks notebooks for performance and scalability. Identify and resolve performance bottlenecks in data processing workflows.


- Data Governance and Security : Ensure data governance and compliance requirements are met by implementing appropriate data security, encryption, and access control mechanisms in Azure data solutions.


- Monitoring and Maintenance : Monitor the performance and health of Azure data solutions, troubleshoot issues, and perform routine maintenance tasks such as data backup and recovery.


- Collaboration : Collaborate with cross-functional teams including data scientists, business analysts, and software developers to understand data requirements and deliver data-driven solutions.


- Documentation : Document technical designs, data pipelines, and implementation details for Azure data solutions. Maintain up-to-date documentation for reference and knowledge sharing.


- Continuous Learning : Stay updated with the latest Azure services, best practices, and industry trends in data engineering and analytics. Continuously enhance technical skills through training and self-learning.

(ref:hirist.tech)

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