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, Event Bridge, Sage Maker, 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, Event Bridge, Cloud Watch, 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.
- Sage Maker, AI Integration & Agentic Enablement
- Integrate AWS data platforms with Amazon Sage Maker for data preparation, feature engineering, model training, deployment, MLOps workflows, and inference-ready data products.
- Support Sage Maker 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, Cloud Trail, 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 Cloud Watch observability, SLA/SLO tracking, alerting, retry logic, restartability, root-cause analysis, incident response, and production reliability management.

Skills and attributes for success

Skill / capability area

Details

- Core platform:AWS Glue, Amazon S3, Athena, Redshift, EMR, MWAA/Airflow, Step Functions, Lambda, Event Bridge, Cloud Watch.
- AI and GenAI: Amazon Sage Maker, Sage Maker 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 Dev Ops:Terraform/Open Tofu, Cloud Formation, Git Hub Actions, Azure Dev Ops, Jenkins, Docker, Kubernetes/EKS, policy-as-code.
- Governance and reliability: IAM, KMS, Lake Formation, Glue Data Catalog, Macie, Immuta, Purview, Cloud Trail, data quality, observability, Data SRE, Fin Ops.

To qualify for the role, you must have

- 8-11 years of experience in data engineering, data platform operations, analytics engineering, platform engineering, or AI platform enablement.




- Strong hands-on implementation experience with cloud data platforms, APIs, Git connectivity, CI/CD, governed access patterns, SRE practices, and production operations.
- Preferred certifications aligned to the relevant cloud/platform stack, data engineering, Dev Ops, security, governance, and AI/ML engineering.

Ideally, you'll also have

- Strong hands-on engineer with architecture awareness, delivery ownership, and a platform engineering mindset.
- Comfortable turning platform standards into reusable frameworks, secure implementation patterns, operational controls, and production-ready services.
- Able to mentor engineers, collaborate with architects/security/SRE teams, and adopt newer AI-native and agentic engineering methods.

What we look for

- Strong hands-on engineer with architecture awareness, delivery ownership, and a platform engineering mindset.
- Comfortable turning platform standards into reusable frameworks, secure implementation patterns, operational controls, and production-ready services.
- Able to mentor engineers, collaborate with architects/security/SRE teams, and adopt newer AI-native and agentic engineering methods.

What working at EY offers

At EY, we're dedicated to helping our clients, from start-ups to Fortune 500 companies, and the work we do with them is as varied as they are.

You get to work with inspiring and meaningful projects. Our focus is education and coaching alongside practical experience to ensure your personal development. We value our employees and you will be able to control your own development with an individual progression plan. You will quickly grow into a responsible role with challenging and stimulating assignments. Moreover, you will be part of an interdisciplinary workplace that emphasises high quality and knowledge exchange. Plus, we offer:

- Support, coaching and feedback from some of the most engaging colleagues around
- Opportunities to develop new skills and progress your career
- The freedom and flexibility to handle your role in a way that's right for you

EY | Building a better working world

📌 EY - GDS Consulting - AI And DATA -AWS Data Engineer - Manager (India)
🏢 EY
📍 India