Join EY's GDS Data and Analytics team as an experienced Data Engineer, specializing in Snowflake, dbt, and Airflow. You will be instrumental in solving complex business challenges for clients by leveraging data and technology, with a focus on the insurance domain. Your expertise will drive the extraction of maximum value from data, uncovering opportunities across various industries.

This role offers the chance to build and optimize scalable data pipelines, ensure data integrity, and enhance analytics capabilities for critical insurance datasets. Contribute to a dynamic environment where innovation and client success are paramount.

Design, build, and maintain robust ELT data pipelines utilizing Snowflake, dbt, and Airflow. Develop and refine data models within Snowflake to meet advanced analytics and reporting needs. Implement intricate data transformation logic using dbt, focusing on business rules and semantic layer creation. Automate and manage workflows and data ingestion processes through Airflow DAGs. Collaborate closely with domain experts to translate insurance-specific data concepts, such as policies and claims, into effective technical pipelines. Proactively monitor system performance, diagnose and resolve data processing issues, and optimize query efficiency. Create comprehensive documentation and conduct knowledge-sharing sessions to foster team growth and best practices.

Demonstrate hands-on expertise with Snowflake for data warehousing solutions. Possess strong proficiency in dbt for creating modular, testable, and maintainable SQL transformations. Exhibit solid experience with Astronomer/Apache Airflow for orchestrating and scheduling data workflows. Showcase strong SQL capabilities and a deep understanding of data modeling principles. Familiarity with ELT best practices and performance optimization techniques is essential. Acquire knowledge of insurance data domains, including policies, claims, and actuarial data. Familiarity with version control systems like Git and CI/CD practices for data projects is advantageous. Experience with cloud environments such as AWS, Azure, or GCP is required. Proficiency in Python for scripting within Airflow workflows is necessary.