Manager - AI Data Platform Architecture

EY; Hyderabad; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

EY-Consulting invites an experienced AWS Data Platform Architect / Engineering Manager to lead architecture, platform ownership and governance of enterprise-scale AWS Data & AI platforms. You will shape data platform strategy, drive modernization, and embed security and SRE practices across AWS-native data services.

Lead API-led integrations, define standards for data ingestion, security, and observability while guiding teams through CI/CD and platform automation to ensure production readiness

Qualifications

• Experience leading enterprise-scale AWS data platforms modernization and migration.
• Ability to define reusable engineering standards and governance.
• Strong collaboration with data security and SRE teams.

Responsibilities

• AWS strategy, architecture and platform ownership across domains.
• Design authority for AWS data platform initiatives and enterprise data products.
• Define target-state architecture using AWS Glue, S3, Athena, Redshift, EMR, MWAA/Airflow, Lambda, EventBridge, Lake Formation.
• Own architecture decisions for scalability, security, governance and production readiness.
• Create platform standards, reference architectures, migration playbooks and governance.

Skills

AWS data platforms

APIs

SageMaker

Data security

Data SRE & agentic operations

Tools

AWS Glue

Amazon S3

Athena

Redshift

EMR

MWAA / Airflow

Lambda

EventBridge

CloudWatch

Lake Formation

Job description

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

The opportunity

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.

APIs, service integration and AWS platform connectivity

• 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.

Git connectivity, DevSecOps and platform automation

• 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.

Data security, Immuta and governed AWS access

• 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.

Data SRE, observability and production operations

• 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.

SageMaker, AI integration and agentic AWS operations

• 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.