Join EY's Global Delivery Services as a Senior Data Engineer, specializing in Oracle Data Integrator (ODI) and AWS. This role is crucial in building, enhancing, and modernizing enterprise data platforms. You will focus on creating scalable ETL/ELT pipelines, leveraging cloud-based data processing, ensuring data quality, and delivering reliable analytics-ready datasets. Become part of a global network that supports EY's growth agenda and shapes the future with confidence.

Design, develop, and support ETL/ELT solutions using Oracle Data Integrator (ODI). Build and maintain AWS data pipelines leveraging services like S3, Glue, and Redshift. Develop and optimize data transformations using SQL, PL/SQL, and PySpark. Create and manage PL/SQL procedures, functions, and packages to support data integration. Analyze and troubleshoot SQL and PL/SQL code, resolve data issues, and assist with performance improvements. Support data ingestion, integration, validation, and deployment. Orchestrate workflows using tools like Apache Airflow and contribute to data lake/lakehouse solutions.

Investigate and resolve pipeline failures to enhance performance and stability. Collaborate with business, analytics, and engineering teams to translate requirements into scalable data solutions. Adhere to engineering standards for documentation, code reviews, version control, testing, and release management. Mentor junior team members and contribute to the continuous improvement of delivery practices.

Possess strong hands-on experience with Oracle Data Integrator (ODI), including mappings, packages, and scenarios. Demonstrate practical AWS data engineering experience with S3, AWS Glue, and Amazon Redshift. Exhibit strong SQL skills and a solid understanding of data warehousing, dimensional modelling, and ETL/ELT concepts. Have working experience in PL/SQL development, including procedures, functions, packages, and query troubleshooting. Experience with PySpark or distributed data processing frameworks is essential. Familiarity with workflow orchestration tools like Apache Airflow is required. Understanding of data quality, logging, monitoring, error handling, and performance optimization is key. The ability to work independently, communicate effectively, and collaborate across distributed teams is vital.