Join EY GDS's Data and Analytics team and leverage data and technology to solve intricate business challenges. We specialize in extracting maximum value from data and identifying opportunities across diverse sectors including Banking, Insurance, Manufacturing, Healthcare, Retail, Auto, Supply Chain, and Finance. This role is designed for experienced professionals passionate about data and eager to grow within a dynamic, supportive global environment.

We are seeking a seasoned Data Engineer with profound expertise in Snowflake, dbt, and Astronomer-Airflow. A strong grasp of insurance business processes is essential for this position. Your focus will be on developing and refining robust data pipelines, ensuring exceptional data quality, and empowering advanced analytics for complex insurance datasets.

Design, build, and maintain scalable ELT data pipelines using Snowflake, dbt, and Airflow. Optimize data models within Snowflake to meet analytics and reporting demands. Implement data transformation logic in dbt for business rules and the creation of a semantic layer. Automate data ingestion and workflow processes using Airflow DAGs. Collaborate with domain specialists to translate insurance data concepts like policies, claims, and underwriting into effective technical pipelines. Monitor system performance, troubleshoot data processing issues, and enhance query efficiency. Prepare comprehensive documentation and conduct knowledge-sharing sessions to benefit the team.

Demonstrate hands-on experience with Snowflake for data warehousing. Possess strong proficiency in dbt for modular and testable SQL transformations. Exhibit solid experience with Astronomer/Apache Airflow for orchestration and scheduling. Showcase strong SQL and data modeling skills. Understand ELT best practices and performance optimization techniques. Possess knowledge of insurance data domains, including policies, claims, actuarial data, and compliance. Familiarity with version control systems like Git and CI/CD practices for data projects is required. Experience with cloud environments such as AWS, Azure, or GCP is necessary. Knowledge of Python for scripting Airflow workflows is also essential.