Note: The job is a remote job and is open to candidates in USA. Precision Technologies is seeking an Azure Data Engineer to design and maintain scalable data pipelines. The role involves building ETL workflows, managing data lakes, and collaborating with various teams to deliver enterprise data solutions.

Responsibilities

• Design, develop, and maintain scalable data pipelines using Azure Data Factory (ADF) and Azure Databricks
• Build and optimize ETL/ELT workflows for ingesting, transforming, and processing large-scale structured and unstructured datasets
• Develop and manage enterprise data lakes using Azure Data Lake Storage (ADLS) and Delta Lake
• Design and optimize data warehouse solutions using Azure Synapse Analytics and Azure SQL Database
• Develop data transformation and analytics solutions using PySpark, Spark SQL, Python, and SQL
• Implement data modeling, dimensional modeling, partitioning, indexing, and performance optimization strategies
• Integrate data from multiple on-premises, cloud, APIs, and third-party data sources
• Monitor, troubleshoot, and optimize data pipelines to ensure high availability, performance, and data quality
• Implement data governance, security, encryption, access controls, and compliance best practices
• Collaborate with Data Scientists, Data Analysts, Business Intelligence teams, and Solution Architects to deliver enterprise data solutions
• Document data architecture, ETL processes, data lineage, and technical specifications while supporting production environments

Skills

• Design, develop, and maintain scalable data pipelines using Azure Data Factory (ADF) and Azure Databricks
• Build and optimize ETL/ELT workflows for ingesting, transforming, and processing large-scale structured and unstructured datasets
• Develop and manage enterprise data lakes using Azure Data Lake Storage (ADLS) and Delta Lake
• Design and optimize data warehouse solutions using Azure Synapse Analytics and Azure SQL Database
• Develop data transformation and analytics solutions using PySpark, Spark SQL, Python, and SQL
• Implement data modeling, dimensional modeling, partitioning, indexing, and performance optimization strategies
• Integrate data from multiple on-premises, cloud, APIs, and third-party data sources
• Monitor, troubleshoot, and optimize data pipelines to ensure high availability, performance, and data quality
• Implement data governance, security, encryption, access controls, and compliance best practices
• Collaborate with Data Scientists, Data Analysts, Business Intelligence teams, and Solution Architects to deliver enterprise data solutions
• Document data architecture, ETL processes, data lineage, and technical specifications while supporting production environments

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