Join EY's GDS Consulting team as an AI Data Platform Engineer on the AWS platform. This role offers the opportunity to build and manage cutting-edge Data & AI platforms, leveraging extensive AWS services. You will play a crucial role in developing robust data engineering and platform engineering solutions, contributing to EY's mission of building a better working world through innovation and technology.

EY's Consulting Services focuses on delivering integrated solutions across various industries, with a strong emphasis on Data and Analytics. The Data and Analytics team tackles complex business challenges, driving better outcomes for clients by embedding analytical practices at the core of their decision-making. This is a chance to shape the future of data-driven insights for leading financial institutions and capital markets participants.

Design, build, and operate production-grade AWS data pipelines using services like S3, Glue, PySpark, Athena, Redshift, EMR, MWAA/Airflow, and Step Functions. Develop versatile ingestion frameworks for batch, streaming, event-driven, and API-based data sources. Construct curated data layers using scalable lakehouse patterns and optimize Spark/Glue/EMR workloads for peak performance and cost-efficiency.

Create reusable AWS platform accelerators for onboarding, monitoring, and deployment. Implement robust CI/CD pipelines and infrastructure as code. Integrate data platforms with enterprise APIs, governance tools, and security platforms. Collaborate closely with infrastructure, IAM, and application teams to resolve operational issues.

Integrate AWS data platforms with Amazon SageMaker for comprehensive MLOps workflows and feature engineering. Support advanced SageMaker features and enable AI-assisted operations for anomaly detection, data quality, and incident summarization. Implement comprehensive AWS data security controls, including IAM least privilege, encryption, and governed access patterns. Own Data SRE responsibilities, ensuring observability, SLA tracking, incident response, and production reliability.

A minimum of 8-11 years of experience in data engineering, platform operations, analytics engineering, or AI platform enablement is required. Demonstrated hands-on experience with cloud data platforms, API integration, Git, CI/CD, secure access patterns, SRE practices, and production operations is essential.

Strong technical proficiency is expected across core AWS data services (Glue, S3, Athena, Redshift, EMR), AI/GenAI tools (SageMaker, Bedrock), and engineering disciplines (Python, PySpark, SQL, Git, CI/CD). Familiarity with cloud and DevOps tools like Terraform, CloudFormation, GitHub Actions, and Docker is highly beneficial.

Experience with governance and reliability tools such as IAM, KMS, Lake Formation, Glue Data Catalog, Macie, Immuta, and CloudTrail is crucial. The ideal candidate possesses a strong engineering mindset, architectural awareness, and a commitment to delivering high-quality, production-ready services. The ability to mentor junior engineers and collaborate effectively with cross-functional teams is also a key attribute.