Data Engineering - Aws

As a Data Engineer with 7-10 years of experience, you will be responsible for the following key tasks: - Implementing Data Engineering solutions in Fabric environment - Integrating GitHub for DevOps processes - Utilizing RBAC for access control - Working with Medallion Architecture and Fabric OneLake - Utilizing ADLS Gen2 Storage for data storage - Utilizing Power Automate and Azure functions for automation - Demonstrating strong SQL skills for querying, transforming, and optimizing large datasets - Proficiency in programming languages such as Python (mandatory) and optionally Scala or Java - Building and maintaining ETL/ELT data pipelines - Demonstrating a solid understanding of data warehousing concepts including star schema, snowflake schema, and dimensional modeling - Working with relational and NoSQL databases - Utilizing cloud data platforms such as AWS, Azure, or GCP - Experience with Big Data processing frameworks, with preference for Apache Spark - Familiarity with workflow orchestration tools like Apache Airflow or equivalent - Knowledge of data modeling, transformation, and validation techniques - Experience with version control systems, specifically Git - Understanding of data security, governance, and access control - Ability to monitor, troubleshoot, and optimize data pipelines in production Additionally, you should have proficiency in the following primary skillset: Azure ADF, DataBricks, Azure Synapse Analytics, Pyspark, Python, and strong SQL skills. As a Data Engineer with 7-10 years of experience, you will be responsible for the following key tasks: - Implementing Data Engineering solutions in Fabric environment - Integrating GitHub for DevOps processes - Utilizing RBAC for access control - Working with Medallion Architecture and Fabric OneLake - Utilizing ADLS Gen2 Storage for data storage - Utilizing Power Automate and Azure functions for automation - Demonstrating strong SQL skills for querying, transforming, and optimizing large datasets - Proficiency in programming languages such as Python (mandatory) and optionally Scala or Java - Building and maintaining ETL/ELT data pipelines - Demonstrating a solid understanding of data warehousing concepts including star schema, snowflake schema, and dimensional modeling - Working with relational and NoSQL databases - Utilizing cloud data platforms such as AWS, Azure, or GCP - Experience with Big Data processing frameworks, with preference for Apache Spark - Familiarity with workflow orchestration tools like Apache Airflow or equivalent - Knowledge of data modeling, transformation, and validation techniques - Experience with version control systems, specifically Git - Understanding of data security, governance, and access control - Ability to monitor, troubleshoot, and optimize data pipelines in production Additionally, you should have proficiency in the following primary skillset: Azure ADF, DataBricks, Azure Synapse Analytics, Pyspark, Python, and strong SQL skills.