Address
Form of workRole- Sr. Azure Data EngineerExperience- 5 to 8 yearsOverview
Primary focus would be to do development work within the platform on Azure Data Lake environment and other related ETL technologies; Satisfying project requirements, while adhering to enterprise architecture standards.Must-have:Good Knowledge of Data Brick lakehouse and Azure DataLake concept
Knowledge of Data Bricks delta concept– Delta live tables (DLT)
Strong hands-on experience in ELT– pipeline development using Azure Data factory and Databricks Autoloader,.
Strong knowledge of metadata-driven data pipeline, metadata management, dynamic logic
In-depth knowledge of data storage solutions, including Azure Data Lake Storage (ADLS), and Azure Serverless SQL Pool.
Experience with data transformation using Spark, and SQL technologies.
Solid understanding of design patterns, and best practices of the cloud stack.
Experience with code management and version control using Git or similar tools.
Strong problem-solving and debugging sk...
Primary focus would be to do development work within the platform on Azure Data Lake environment and other related ETL technologies; Satisfying project requirements, while adhering to enterprise architecture standards.Must-have:Good Knowledge of Data Brick lakehouse and Azure DataLake concept
Knowledge of Data Bricks delta concept– Delta live tables (DLT)
Strong hands-on experience in ELT– pipeline development using Azure Data factory and Databricks Autoloader,.
Strong knowledge of metadata-driven data pipeline, metadata management, dynamic logic
In-depth knowledge of data storage solutions, including Azure Data Lake Storage (ADLS), and Azure Serverless SQL Pool.
Experience with data transformation using Spark, and SQL technologies.
Solid understanding of design patterns, and best practices of the cloud stack.
Experience with code management and version control using Git or similar tools.
Strong problem-solving and debugging sk...
Role- Sr. Azure Data EngineerExperience- 5 to 8 yearsOverview
Primary focus would be to do development work within the platform on Azure Data Lake environment and other related ETL technologies; Satisfying project requirements, while adhering to enterprise architecture standards.Must-have:Good Knowledge of Data Brick lakehouse and Azure DataLake concept
Knowledge of Data Bricks delta concept– Delta live tables (DLT)
Strong hands-on experience in ELT– pipeline development using Azure Data factory and Databricks Autoloader,.
Strong knowledge of metadata-driven data pipeline, metadata management, dynamic logic
In-depth knowledge of data storage solutions, including Azure Data Lake Storage (ADLS), and Azure Serverless SQL Pool.
Experience with data transformation using Spark, and SQL technologies.
Solid understanding of design patterns, and best practices of the cloud stack.
Experience with code management and version control using Git or similar tools.
Strong problem-solving and debugging skills in ETL workflows and data pipelines.
Strong understanding of Azure Data bricks– features and capabilities.
Knowledge of Azure DevOps and continuous integration and deployment (CI/CD) process.
Knowledge of data quality and data profiling techniques, with experience in data validation and data cleansing.
Hands-on Duties:Conducting technical sessions, design reviews, code reviews, and demos of pipelines and their functionality
Developing technical specification for Data pipelines and workflow and getting sign-off from Architect.
Developing, deploying, and maintaining workflows and data pipelines using Azure Data bricks.
Collaborating with data architects, data analysts, and other stakeholders to design and implement ETL solutions that meet business requirements.
Writing efficient and high-performing ETL code using PySpark, and SQL technologies.
Building and testing data pipelines using Azure Data bricks.
Ensuring the accuracy, completeness, and timeliness of data being processed and integrated.
Troubleshooting and resolving issues related to data pipelines and notebooks.
Performance benchmarking of data ingestion and Data flow pipeline/notebook and ensuring consistency
Primary focus would be to do development work within the platform on Azure Data Lake environment and other related ETL technologies; Satisfying project requirements, while adhering to enterprise architecture standards.Must-have:Good Knowledge of Data Brick lakehouse and Azure DataLake concept
Knowledge of Data Bricks delta concept– Delta live tables (DLT)
Strong hands-on experience in ELT– pipeline development using Azure Data factory and Databricks Autoloader,.
Strong knowledge of metadata-driven data pipeline, metadata management, dynamic logic
In-depth knowledge of data storage solutions, including Azure Data Lake Storage (ADLS), and Azure Serverless SQL Pool.
Experience with data transformation using Spark, and SQL technologies.
Solid understanding of design patterns, and best practices of the cloud stack.
Experience with code management and version control using Git or similar tools.
Strong problem-solving and debugging skills in ETL workflows and data pipelines.
Strong understanding of Azure Data bricks– features and capabilities.
Knowledge of Azure DevOps and continuous integration and deployment (CI/CD) process.
Knowledge of data quality and data profiling techniques, with experience in data validation and data cleansing.
Hands-on Duties:Conducting technical sessions, design reviews, code reviews, and demos of pipelines and their functionality
Developing technical specification for Data pipelines and workflow and getting sign-off from Architect.
Developing, deploying, and maintaining workflows and data pipelines using Azure Data bricks.
Collaborating with data architects, data analysts, and other stakeholders to design and implement ETL solutions that meet business requirements.
Writing efficient and high-performing ETL code using PySpark, and SQL technologies.
Building and testing data pipelines using Azure Data bricks.
Ensuring the accuracy, completeness, and timeliness of data being processed and integrated.
Troubleshooting and resolving issues related to data pipelines and notebooks.
Performance benchmarking of data ingestion and Data flow pipeline/notebook and ensuring consistency
