Join EY's GDS Consulting team as an AI Data Engineer Manager and contribute to building cutting-edge AWS-native Data & AI platforms. This role offers the opportunity to shape the future of data and analytics within financial services and beyond, leveraging deep industry experience and strong technical capabilities. You'll be instrumental in creating compelling business cases for analytical practices at the heart of client decision-making, fostering growth and safeguarding businesses.

Design and implement robust AWS data pipelines using services like S3, Glue, PySpark, Athena, Redshift, EMR, and MWAA. Develop reusable ingestion frameworks for various data patterns, including batch, streaming, and event-driven. Build curated data layers with scalable lakehouse architectures, optimizing Spark/Glue/EMR workloads for performance and cost. Create AWS platform accelerators for onboarding, monitoring, and deployment. Integrate data platforms with enterprise APIs, source applications, and downstream consumers. Enable AI/ML capabilities by integrating with Amazon SageMaker and supporting MLOps workflows. Implement comprehensive data security controls and Data SRE practices, including observability, incident response, and production reliability.

Candidates should possess 8-11 years of experience in data engineering, platform operations, or AI platform enablement, with a strong track record in cloud data platforms, APIs, Git, CI/CD, and SRE practices. Expertise in AWS services such as Glue, S3, Redshift, EMR, and SageMaker is essential. Proficiency in Python, PySpark, SQL, and DevOps tools like Terraform and Jenkins is required. Familiarity with data governance tools like Lake Formation and Immuta, along with strong Data SRE and FinOps skills, is highly beneficial.