Role

AWS Data Platform Architect / Engineering Manager - Experience Guide

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 setting 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.

Skills and attributes for success

- Core AWS platform - AWS Glue, Amazon S3, Athena, Redshift, EMR, MWAA/Airflow, Step Functions, Lambda, EventBridge, Kinesis, CloudWatch, Lake Formation.

- APIs and integration - REST APIs, API Gateway, Lambda integration

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.

📌 GDS Consulting - AI And DATA -AI Data Platform Architect/Lead (Hyderabad)
🏢 EY
📍 Hyderabad