Join EY's GDS Consulting team as an AI Data Platform Engineer - Manager, focusing on building and operating cutting-edge AWS-native Data & AI platforms. This role is pivotal in leveraging strong data and platform engineering ownership to drive innovation.

EY's Consulting Services offers a unique, industry-focused approach, providing integrated solutions that combine deep industry experience with robust functional, technical, and product capabilities. Our Data and Analytics team tackles complex challenges, creating opportunities for better business outcomes and safeguarding client futures by embedding advanced analytics into core decision-making.

This is an exceptional opportunity to contribute to a leading global organization committed to fostering individual growth and building a better working world through technology and diverse expertise.

Design and implement robust, production-grade AWS data pipelines using a suite of services including S3, Glue, PySpark, Athena, Redshift, EMR, MWAA/Airflow, and Step Functions. Develop reusable ingestion frameworks for diverse data patterns like batch, streaming, and event-driven data. Construct curated data layers—raw, standardized, trusted, and consumption-ready—using scalable lakehouse architectures, optimizing for performance and efficiency.

Build and deploy reusable AWS platform accelerators for onboarding, pipeline templating, orchestration, monitoring, and deployment. Implement modern DevOps practices with Git-based workflows, CI/CD, Infrastructure as Code, and controlled release processes. Seamlessly integrate AWS data platforms with enterprise APIs, source systems, messaging queues, and governance tools.

Enable advanced AI and GenAI capabilities by integrating with Amazon SageMaker for data preparation, feature engineering, model training, MLOps, and deploying inference-ready data products. Explore AI-assisted operations for anomaly detection, schema drift, incident summarization, and automated data quality recommendations.

Enforce stringent AWS data security controls, including IAM least privilege, KMS encryption, and Lake Formation governance. Integrate with leading governance and security platforms like Immuta and Microsoft Purview. Own Data SRE responsibilities, focusing on observability, SLA/SLO tracking, incident response, and ensuring production reliability.

A seasoned professional with 8-11 years of dedicated experience in data engineering, data platform operations, analytics engineering, or AI platform enablement. Demonstrable hands-on expertise in implementing cloud data platforms, API integrations, Git-based CI/CD pipelines, secure access patterns, SRE practices, and production operational management is essential.

Proficiency in core AWS data services such as Glue, S3, Athena, Redshift, and EMR, alongside experience with orchestration tools like MWAA/Airflow and Step Functions, is expected. Expertise in Python, PySpark, SQL, and scripting languages is fundamental.

Familiarity with DevOps tools and practices, including Terraform/OpenTofu, CloudFormation, GitHub Actions, and Docker/Kubernetes, is highly beneficial. A strong understanding of cloud security principles, IAM, KMS, and data governance frameworks like Lake Formation and Immuta is crucial.

Possessing relevant cloud or data engineering certifications is preferred. An architecturally aware mindset, ownership of delivery, and a passion for platform engineering are key attributes, along with the ability to mentor junior engineers and collaborate effectively with cross-functional teams.