Join EY's AI and Data (AI&D) team as a Senior Data Engineer, shaping the future of enterprise data platforms and analytics. This role is integral to designing and implementing scalable data solutions that empower advanced analytics and AI-driven initiatives across diverse industries. You'll collaborate with global, multi-disciplinary teams, contributing to EY's mission of building a better working world.

Leverage your expertise to deliver robust, production-grade data systems, ensuring high data quality and enabling business intelligence. Your work will directly support critical business functions and drive innovation through data.

This is an exceptional opportunity for professional growth, offering world-class learning, tailored coaching, and the chance to develop into an outstanding leader within a dynamic, inclusive, and global environment.

As a Senior Data Engineer, you will be instrumental in architecting and developing resilient, production-ready data pipelines using Azure Data Factory and Azure Databricks. You'll craft scalable data architectures with Azure SQL and Data Lake, employing modern data modeling techniques like star and snowflake schemas.

Your responsibilities extend to implementing rigorous data quality validation and reconciliation processes, actively resolving operational issues. You will utilize Python for complex data processing, transformation, and analytics, and maintain Jupyter Notebooks for efficient exploration and experimentation. Furthermore, you'll employ MLflow for comprehensive experiment tracking and model lifecycle management.

Key contributions include delivering optimized, analytics-ready datasets for Power BI and other reporting tools, and implementing CI/CD pipelines to ensure seamless data engineering workflows. You will also uphold secure data handling practices, monitor system performance, and troubleshoot pipeline failures.

We are seeking experienced Senior Data Engineers with a minimum of 5 to 8 years of professional experience. Essential expertise includes Azure Data Factory, Azure Databricks, Azure SQL, and Azure Data Lake. A solid understanding of data modeling principles, particularly star and snowflake schemas, is crucial.

Proven experience in building scalable and reliable data pipelines is a must. You should possess strong problem-solving and analytical skills, with a demonstrated ability to manage production-grade data environments. A keen focus on data quality, scalability, and performance optimization is paramount for success in this role.

Candidates with experience in regulated data environments, exposure to Azure OpenAI, or AI/ML use cases, including prompt engineering and evaluation techniques, will be highly regarded. This role offers a fantastic platform to advance your career within a leading global organization.