Join EY's GDS Consulting team as an AI Data Platform Engineer specializing in AWS. This senior role involves building and managing robust AWS-native Data & AI platforms. You'll leverage deep expertise in data engineering and platform ownership to create exceptional experiences for clients.

EY's Consulting Services offers integrated solutions for financial institutions, focusing on Data and Analytics. Our team tackles complex challenges, driving better business outcomes through embedding advanced analytical practices into client decision-making.

Key responsibilities include designing and implementing production-grade AWS data pipelines using services like S3, Glue, PySpark, Athena, and Redshift. You will develop reusable ingestion frameworks, build curated data layers using lakehouse patterns, and optimize Spark/Glue/EMR workloads. Additionally, you will create AWS platform accelerators, implement Git-based delivery, and integrate data platforms with enterprise APIs and security tools.

The role also encompasses integrating with Amazon SageMaker for MLOps workflows and data preparation, supporting AI-assisted operations for anomaly detection and incident summarization. Strong emphasis is placed on implementing AWS data security controls, integrating with governance tools like Immuta, and fulfilling Data SRE responsibilities for production reliability.

We seek a seasoned professional with 5-10 years of experience in data engineering, data platform operations, or AI platform enablement. Essential skills include strong hands-on implementation experience with cloud data platforms, APIs, Git, CI/CD, governed access, SRE practices, and production operations.

Ideal candidates possess an engineering mindset, architecture awareness, and delivery ownership. You should be comfortable transforming platform standards into reusable frameworks and operational controls. The ability to mentor engineers and collaborate effectively with cross-functional teams is highly valued.