Role
AI Data Platform Engineer - AWS
Experience Guide
5-10 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 setting 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 Integr
📌 Sr GDS Consulting (Hyderabad)
🏢 EY
📍 Hyderabad