Embark on a career-defining journey with EY GDS, where you'll contribute to solving complex business challenges through cutting-edge data and technology solutions. Our Data and Analytics team collaborates across diverse industries, including Banking, Insurance, Manufacturing, Healthcare, Retail, Supply Chain, and Finance. We specialize in delivering scalable and innovative data solutions that drive business value.

This senior role focuses on modern data engineering practices, demanding hands-on expertise in building and managing comprehensive data pipelines. You'll be instrumental in data transformation using dbt, working with Google BigQuery, and orchestrating workflows with Apache Airflow. A strong foundation in data warehousing, schema design (Star/Snowflake), and preferably experience in Wealth & Asset Management (WAM) data domains, will set you up for success.

As a Senior Data Engineer, you will be responsible for designing, building, and managing robust end-to-end data pipelines. You will develop scalable and maintainable data transformation workflows utilizing dbt, and actively participate in the entire software and data development lifecycle. Key activities include collaborating with cross-functional teams to gather business requirements, ensuring data quality and platform performance, and writing clean, reusable code adhering to engineering best practices.

Further responsibilities involve working with structured and semi-structured data sources to support advanced analytics and reporting. You will also troubleshoot and optimize existing data pipelines and workflows, ensuring high-quality software and data engineering solutions through rigorous testing and maintenance, primarily using Python.

We are seeking professionals with a Bachelor of Engineering (B.E.), Bachelor of Technology (B.Tech), or Master of Computer Applications (MCA) degree, complemented by 3 to 5 years of relevant industry experience in Data Engineering. Your technical toolkit should include proven experience with dbt for data transformation and modeling, alongside expertise in constructing and managing end-to-end data pipelines.

Essential proficiencies include strong SQL skills and in-depth experience with Google BigQuery. Experience in workflow orchestration using Apache Airflow is critical. A solid understanding of Data Warehousing, ETL/ELT processes, and both Star and Snowflake schema design is required. You must be proficient in Python for data engineering tasks and automation, demonstrating excellent problem-solving, analytical, and communication abilities. The capacity to rapidly learn new technologies and adapt to a dynamic development environment is also key.