EY - GDS Consulting - AI And DATA -AI Data Platform Engineer - AWS - Senior

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all.

EY-Consulting - Data and Analytics - AI Data Platform Engineer - AWS - Senior EY's Consulting Services is a unique, industry-focused business unit that provides a broad range of integrated services that leverage deep industry experience with strong functional and technical capabilities and product knowledge. EY's financial services practice provides integrated Consulting services to financial institutions and other capital markets participants, including commercial banks, retail banks, investment banks, broker-dealers & asset management firms, and insurance firms from leading Fortune 500 Companies. Within EY's Consulting Practice, Data and Analytics team solves big, complex issues and capitalise on opportunities to deliver better working outcomes that help expand and safeguard the businesses, now and in the future. This way we help create a compelling business case for embedding the right analytical practice at the heart of client's decision-making.   Role - AI Data Platform Engineer - AWS             Experience Guide - 5-10 years Primary Skill Area - AWS Data Platforms, Glue, EMR, SageMaker, S3, Redshift, Event-Driven Data & Agentic Operations   The opportunity Build and operate AWS-native Data & AI platforms with strong data engineering and platform engineering ownership. The role focuses on AWS Glue, EMR, S3, Athena, Redshift, MWAA, Step Functions, Lambda, EventBridge, SageMaker, APIs, enterprise service integration, Git-based delivery, Data SRE, data security, Immuta/Lake Formation governed access, and AI/agentic operations for production-grade data and AI workloads.   Your key responsibilities AWS Data Engineering

Design and build production-grade AWS data pipelines using Amazon S3, AWS Glue, PySpark, Athena, Redshift, EMR, MWAA/Airflow, Step Functions, Lambda, EventBridge, CloudWatch, IAM, and KMS. Develop reusable ingestion frameworks supporting batch, streaming, event-driven, CDC, API-based, file-based, database, and third-party service integration patterns. Build curated raw, standardised, trusted, and consumption layers using scalable lakehouse design patterns, partitioning, metadata management, and file-format optimisation. Optimise Spark/Glue/EMR workloads for performance, cost efficiency, scalability, and operational stability. AWS Platform Engineering Create reusable AWS platform accelerators for onboarding, pipeline templates, orchestration, monitoring, reconciliation, deployment, logging, and support runbooks. Implement Git connectivity, branching strategy, pull requests, code reviews, CI/CD, Infrastructure as Code, controlled releases, and environment promotion. Integrate AWS data platforms with enterprise APIs, source applications, messaging/event services, governance tools, security platforms, and downstream analytics consumers. Partner with infrastructure, IAM, network, DBA, application, and support teams to resolve connectivity, access, deployment, and production issues. SageMaker, AI Integration & Agentic Enablement Integrate AWS data platforms with Amazon SageMaker for data preparation, feature engineering, model training, deployment, MLOps workflows, and inference-ready data products. Support SageMaker Pipelines, Feature Store, Model Registry, Model Monitor, Bedrock where relevant, vector stores, semantic search, and RAG-ready data products. Apply AI-assisted and agentic operations for anomaly detection, schema drift detection, failed-job diagnosis, data quality recommendations, documentation generation, and incident summarisation. Governance, Security & Data SRE Implement AWS