Job
Description
As an experienced Azure Databricks Engineer with expertise in Snowflake and modern data engineering tools, your role will involve designing, developing, and optimizing scalable data pipelines for efficient data integration, transformation, and analytics delivery across Azure-based ecosystems. Key Responsibilities: - Design and implement ETL/ELT pipelines using Azure Databricks, PySpark, and Snowflake. - Develop and maintain data ingestion and transformation workflows from various sources such as SQL, APIs, and cloud storage. - Integrate Databricks with Azure Data Factory (ADF), Azure Synapse, and Snowflake for seamless data orchestration. - Optimize data models, query performance, and storage architecture to ensure scalability and efficiency. - Ensure data quality, validation, and governance following best practices. - Collaborate with data analysts, scientists, and BI teams to provide reliable datasets for analytics and reporting. - Implement monitoring, debugging, and performance tuning of data pipelines and workflows. - Maintain documentation and participate in code reviews to uphold consistency and quality. Qualifications: - Bachelor's or Master's degree in Computer Science, Information Technology, or a related field. - 5+ years of hands-on experience in data engineering with a minimum of 2+ years working on Azure Databricks and Snowflake. - Possess strong problem-solving, analytical, and collaboration skills. Technical Skills Required: - Core: Azure Databricks, PySpark, Snowflake - Azure Services: Data Factory (ADF), Synapse, Azure Blob Storage, Azure Data Lake - Programming Languages: Python, SQL - Data Modeling: Star/Snowflake schema design, performance optimization - Version Control: Git / Azure DevOps - Bonus Skills: Power BI integration, Delta Lake, CI/CD for Data Pipelines With these qualifications and technical skills, you will play a crucial role in designing and optimizing data pipelines, ensuring efficient data integration and analytics delivery within Azure-based ecosystems. As an experienced Azure Databricks Engineer with expertise in Snowflake and modern data engineering tools, your role will involve designing, developing, and optimizing scalable data pipelines for efficient data integration, transformation, and analytics delivery across Azure-based ecosystems. Key Responsibilities: - Design and implement ETL/ELT pipelines using Azure Databricks, PySpark, and Snowflake. - Develop and maintain data ingestion and transformation workflows from various sources such as SQL, APIs, and cloud storage. - Integrate Databricks with Azure Data Factory (ADF), Azure Synapse, and Snowflake for seamless data orchestration. - Optimize data models, query performance, and storage architecture to ensure scalability and efficiency. - Ensure data quality, validation, and governance following best practices. - Collaborate with data analysts, scientists, and BI teams to provide reliable datasets for analytics and reporting. - Implement monitoring, debugging, and performance tuning of data pipelines and workflows. - Maintain documentation and participate in code reviews to uphold consistency and quality. Qualifications: - Bachelor's or Master's degree in Computer Science, Information Technology, or a related field. - 5+ years of hands-on experience in data engineering with a minimum of 2+ years working on Azure Databricks and Snowflake. - Possess strong problem-solving, analytical, and collaboration skills. Technical Skills Required: - Core: Azure Databricks, PySpark, Snowflake - Azure Services: Data Factory (ADF), Synapse, Azure Blob Storage, Azure Data Lake - Programming Languages: Python, SQL - Data Modeling: Star/Snowflake schema design, performance optimization - Version Control: Git / Azure DevOps - Bonus Skills: Power BI integration, Delta Lake, CI/CD for Data Pipelines With these qualifications and technical skills, you will play a crucial role in designing and optimizing data pipelines, ensuring efficient data integration and analytics delivery within Azure-based ecosystems.
