EY - GDS Consulting - AI And DATA -AWS Data Engineer - Manager
Ernst Young · Kolkata · onsite · Posted today
Undisclosed8–11 yrsfulltimeSourced from
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About this role
Job Summary
The opportunity
Your key responsibilities
AWS Data Engineering
AWS Platform Engineering
Governance, Security & Data SRE
Skills and attributes for success
Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
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
- 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.
Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
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Skills
Aws GlueCdcAzure DevopsBedrockai integrationapisAsset Managementamazon web servicesAWS data engineeramazon s3aws cloudformationapache airflowAnalyticsAnomaly DetectionCloud DataCapital MarketscloudArchitectureAi PlatformMicrosoft AzureArtificial Intelligence
About Ernst & Young
Ernst Young
Management Consulting
44k+
employees
2002
24 years old
Gurgaon / Gurugram, Haryana
India
₹15.3L PA avg
Avg at Ernst & Young
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