HYDERABAD · FULLTIME
Manager - AI Data Platform Architecture
Ernst Young
Hyderabad · onsite · Posted 4d ago
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Undisclosed10–15 yrsfulltimeHyderabad
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Section · 01
About this role
Job Summary
EY-Consulting - Data and Analytics - AWS Data Platform Architect / Engineering Manager - / Manager / Lead Architect
Role AWS Data Platform Architect / Engineering Manager - Experience Guide
- Guide / 10+ years Primary Skill Area
- AWS Data Platforms, APIs, SageMaker, Data Security, Data SRE & Agentic Operations
Provide architecture leadership, platform ownership and delivery governance for enterprise-scale AWS Data & AI platforms. The role is responsible for shaping AWS data platform strategy, leading modernisation and migration programmes, defining reusable engineering standards, integrating APIs and enterprise services, establishing Git/CI-CD practices, embedding Data SRE, strengthening data security and enabling agentic/AI-assisted operations across AWS-native data platforms.
Your key responsibilities - AWS strategy, architecture and platform ownership
- Serve as design authority for AWS data platform initiatives across multiple domains, programmes and enterprise data products.
- Define target-state architecture using AWS Glue, Amazon S3, Athena, Redshift, EMR, MWAA/Airflow, Step Functions, Lambda, EventBridge, CloudWatch, Lake Formation and lakehouse patterns.
- Own architecture decisions for scalability, resilience, security, governance, observability, performance, maintainability, FinOps and production readiness.
- Create platform standards, reference architectures, reusable frameworks, migration playbooks and engineering governance for AWS data platforms.
- Lead API-led and service-based integration patterns across source systems, enterprise applications, SaaS platforms, data catalogues, governance tools, messaging services and downstream analytics/AI consumers.
- Define standards for REST APIs, event-driven ingestion, CDC, streaming, file ingestion, database integration, authentication, retries, error handling and dependency monitoring.
- Integrate AWS data platforms with enterprise services such as IAM, secrets management, network controls, monitoring, ticketing, metadata, lineage, access workflows and DevOps tools.
- Drive reusable integration frameworks using API Gateway, Lambda, EventBridge, Step Functions, Glue, EMR, MWAA, Kinesis and managed event-driven patterns where appropriate.
- Establish Git connectivity across code repositories, branching strategy, pull requests, code reviews, release controls, artefact versioning and environment promotion.
- Lead CI/CD and Infrastructure-as-Code adoption using GitHub, Azure DevOps, Jenkins, Terraform/OpenTofu, CloudFormation and policy-as-code practices.
- Define automated testing, code scanning, dependency scanning, secrets management, configuration management, deployment validation and production release documentation standards.
- Promote automation for environment setup, monitoring, reconciliation, deployment validation, compliance evidence and support handover.
- Drive AWS security practices including IAM least privilege, KMS encryption, Secrets Manager, VPC endpoints, CloudTrail, logging, controlled data handling and audit-ready access patterns.
- Define governed access models using AWS Lake Formation, Glue Data Catalog, Macie, Immuta, Microsoft Purview, Collibra and enterprise IAM/access workflow tools as relevant.
- Implement controls for RBAC/ABAC, dynamic masking, row/column-level access, data classification, lineage, data residency, privacy and policy enforcement.
- Partner with cyber, privacy, risk, infrastructure and architecture teams to ensure AWS platform designs are secure, compliant and production-ready.
- Establish Data SRE practices for AWS data pipelines, lakehouse services, orchestration, ML/AI workloads and platform operations.
- Define observability dashboards covering Glue/EMR/MWAA job health, data freshness, data quality, SLA/SLO, cost, capacity, access activity, incident trends and dependency failures.
- Act as escalation point for complex production issues, root-cause analysis, recurring incident elimination, performance tuning and long-term platform improvement.
- Drive restartability, retry logic, alert rationalisation, runbooks, service transition, support operating model and operational continuity.
- Integrate AWS data platforms with Amazon SageMaker for data preparation, feature engineering, model training, deployment, model monitoring, MLOps and inference-ready data products.
- Support AI/GenAI workloads using SageMaker, Bedrock where relevant, vector databases, knowledge bases, RAG, GraphRAG, model evaluation and enterprise AI governance patterns.
- Drive agentic and AI-assisted operations for metadata discovery, mapping, lineage, data quality rule generation, anomaly detection, failure diagnosis, automated remediation and self-healing workflows.
- Ensure AI-enabled AWS data solutions remain secure, governed, explainable, observable and aligned with enterprise risk controls.
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Section · 02
Skills
Aws GlueCdcAutomationAzure DevopsBedrockai integrationapisAsset Managementautomated testingamazon web servicesamazon s3apache airflowai governanceAnalyticsAnomaly DetectionCapital MarketsAPI GatewaysAws SecurityArchitectureMicrosoft AzureArtificial Intelligence
Section · Company
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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At EY, our purpose is Building a better working world. The insights and quality services we provide help build trust and confidence in the capital markets and in economies the world over.
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