EY - GDS Consulting - AI And DATA -AWS Data Engineer - Manager

Job description

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 - Manager 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 8-11 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 data security controls including IAM least privilege, KMS encryption, Secrets Manager, VPC endpoints, Lake Formation, Glue Data Catalog, Macie, CloudTrail, and audit-ready access patterns. Integrate with Immuta, Microsoft Purview, Collibra, enterprise IAM, monitoring platforms, data quality tools, and downstream analytics/AI consumers. Own Data SRE responsibilities including CloudWatch observability, SLA/SLO tracking, alerting, retry logic, restartability, root-cause analysis, incident response, and production reliability management. Skills and attributes for success Skill / capability area Details Core platform: AWS Glue, Amazon S3, Athena, Redshift, EMR, MWAA/Airflow, Step Functions, Lambda, EventBridge, CloudWatch. AI and GenAI: Amazon SageMaker, SageMaker Pipelines, Feature Store, Model Registry, Model Monitor, Bedrock optional, RAG-ready data products, Agentic AI. Engineering: Python, PySpark, SQL, APIs, Git, CI/CD, Shell scripting, unit testing, integration testing, data pipeline testing. Cloud and DevOps: Terraform/OpenTofu, CloudFormation, GitHub Actions, Azure DevOps, Jenkins, Docker, Kubernetes/EKS, policy-as-code. Governance and reliability: IAM, KMS, Lake Formation, Glue Data Catalog, Macie, Immuta, Purview, CloudTrail, data quality, observability, Data SRE, FinOps. To qualify for the role, you must have 8-11 years of experience in data engineering, data platform operations, analytics engineering, platform engineering, or AI platform enablement. Strong hands-on implementation experience with cloud data platforms, APIs, Git connectivity, CI/CD, governed access patterns, SRE practices, and production operations. Preferred certifications aligned to the relevant cloud/platform stack, data engineering, DevOps, security, governance, and AI/ML engineering. Ideally, you'll also have Strong hands-on engineer with architecture awareness, delivery ownership, and a platform engineering mindset. Comfortable turning platform standards into reusable frameworks, secure implementation patterns, operational controls, and production-ready services. Able to mentor engineers, collaborate with architects/security/SRE teams, and adopt newer AI-native and agentic engineering methods. What we look for Strong hands-on engineer with architecture awareness, delivery ownership, and a platform engineering mindset. Comfortable turning platform standards into reusable frameworks, secure implementation patterns, operational controls, and production-ready services. Able to mentor engineers, collaborate with architects/security/SRE teams, and adopt newer AI-native and agentic engineering methods. What working at EY offers At EY, we're dedicated to helping our clients, from start-ups to Fortune 500 companies, and the work we do with them is as varied as they are. You get to work with inspiring and meaningful projects. Our focus is education and coaching alongside practical experience to ensure your personal development. We value our employees and you will be able to control your own development with an individual progression plan. You will quickly grow into a responsible role with challenging and stimulating assignments. Moreover, you will be part of an interdisciplinary environment that emphasises high quality and knowledge exchange. Plus, we offer: Support, coaching and feedback from some of the most engaging colleagues around Opportunities to develop new skills and progress your career The freedom and flexibility to handle your role in a way that's right for you EY | Building a better working world EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate. Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.