Cloud Data Specialist

1 day ago

Hyderabad, Telangana, India beBeeDataEngineer Full time US$ 1,50,000 - US$ 1,80,000

Job Title: Cloud Data Specialist

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We are seeking a highly skilled and experienced Cloud Data Specialist to join our team. The ideal candidate will have a strong background in building and maintaining data pipelines and working with large datasets on cloud platforms.

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The successful candidate will be responsible for designing, developing, and implementing efficient ETL processes, working with data warehouses, and leveraging cloud technologies such as Azure Data Factory (ADF), Azure Databricks, PySpark, and SQL to process and transform data for analytical purposes.

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Main Responsibilities:

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  • Data Pipeline Development: Design, develop, and implement scalable, reliable, and high-performance data pipelines using Azure Data Factory (ADF), Azure Databricks, and PySpark.\
  • Data Processing: Develop complex data transformations, aggregations, and cleansing processes using PySpark and Databricks for big data workloads.\
  • Data Integration: Integrate and process data from various sources such as databases, APIs, cloud storage (e.g., Blob Storage, Data Lake), and third-party services into cloud-based data services.\
  • Optimization: Optimize data workflows and ETL processes to ensure efficient data loading, transformation, and retrieval while ensuring data integrity and high performance.\
  • SQL Development: Write complex SQL queries for data extraction, aggregation, and transformation. Maintain and optimize relational databases and data warehouses.\
  • Collaboration: Work closely with data scientists, analysts, and other engineering teams to understand data requirements and design solutions that meet business and analytical needs.\
  • Automation & Monitoring: Implement automation for data pipeline deployment and ensure monitoring, logging, and alerting mechanisms are in place for pipeline health.\
  • Cloud Infrastructure Management: Work with cloud technologies (e.g., Azure Data Lake, Blob Storage) to store, manage, and process large datasets.\
  • Documentation & Best Practices: Maintain thorough documentation of data pipelines, workflows, and best practices for data engineering solutions.\

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