EY is seeking a Data Analytics Testing Senior to join their Consulting team. This role involves ensuring the quality of Business Intelligence and Data Warehousing solutions across on-premise and cloud environments. You will work closely with the Test Manager on project testing, leveraging your expertise in ETL, data warehousing, and Business Intelligence. The position offers opportunities to work on inspiring projects, receive coaching, and develop your career within a supportive and inclusive culture. EY aims to build a better working world by helping clients grow, transform, and operate, enabled by data and technology.

Develop comprehensive test strategies for BI & DWH projects, covering ETL and report testing, integration, and UAT. Design manual and automated test cases, and identify automation opportunities. Ensure test readiness, including environment, data, and tools. Coordinate testing efforts with developers, data engineers, business analysts, and project managers to meet project timelines. Establish and enforce data quality standards, and perform data profiling and cleansing. Deliver testing for BI & DWH projects, including Big Data testing on-premise and cloud platforms. Review requirements, perform test execution, report progress, and manage defect resolution. Prepare test reports, provide inputs for sign-off, and support client status reporting. Contribute to test metrics analysis and implement improvements. Manage changes and conduct regression testing, generating comprehensive test summary reports. Lead teams across geographies and contribute to Analytics and Advanced Analytics Testing training.

A Bachelor's degree in Engineering/Technology (BE/B.Tech), BCA, or M.Sc is required. The role demands 5-8 years of overall testing experience in Data Warehousing/Business Intelligence solutions, with at least 3 years focused on BI & DWH technologies and Analytics applications. Proficiency in Data Warehouse testing, ETL, and BI is essential, along with exposure to reporting tools like Cognos, PowerBI, Tableau, or Qlik. Experience with ETL tools such as Informatica, SSIS, SAP Data Services, AWS Glue, or Azure Data Factory is necessary. Strong SQL skills for complex data validation queries on databases like Oracle, SQL Server, Teradata, Snowflake, Postgres, or Redshift are crucial. Familiarity with data services of cloud providers (AWS, Azure, GCP) and data platforms like Databricks or Azure Synapse is expected. Experience in validating data integrity across ETL pipelines is key. While not mandatory, experience in Master Data Management (MDM) and working with Python for data processing are advantageous. Experience in Big Data testing with Hadoop/Spark and exposure to predictive analytics testing are desirable.