New opportunity

AWS Sagemaker/ Bedrock, Data Engineer, ETL, Snowflake

NTTD Info Pro Ser Pte Ltd · hyderabad, India

About this role

NTT DATA is seeking an experienced Data Engineer to join their team in Hyderabad, India. This role focuses on building and supporting enterprise-scale AI/ML platforms and data pipelines. Responsibilities include designing and implementing scalable technical solutions for AI/ML requirements, ensuring data quality, and managing data governance, security, and privacy. The role requires collaboration across Data Engineering, Data Science, ML Engineering, Cloud, and DevOps teams to ensure operational excellence, automation, and reliability. Key responsibilities include: Supporting enterprise-scale AI/ML platforms; Translating business and AI/ML requirements into scalable technical solutions; Troubleshooting complex production issues; Managing data governance, security, and regulatory requirements. Required Qualifications: 8+ years of experience in DWH/BI technology; Proven experience with AI/ML solutioning using Amazon Sagemaker, AWS Bedrock, dbt, and Snowflake; Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Information Technology, or a related field; Strong analytical, problem-solving, communication, and stakeholder-management skills. Preferred Qualifications: Experience with healthcare/clinical data or Carepath-related applications; Experience with GenAI, LLMOps, RAG pipelines, vector databases, or AI agents; AWS certification. The primary technology stack includes Snowflake, SQL, dbt, AWS (S3, Glue, Redshift, Lambda, SageMaker, CloudWatch, Bedrock), Python, and various DevOps tools like Git, CI/CD, Docker, and Terraform.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

AWS SagemakerAWS BedrockSnowflakedbtData WarehousingETLELTDimensional ModelingData QualityPythonSQLMachine LearningMLOpsGenerative AILLMsRAGLLMOpsAWS S3AWS GlueAWS RedshiftAWS LambdaCloudWatchFiddler ExchangeGitCI/CDDockerTerraform

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Architecting Scalable AI/ML Data Pipelines
The role requires designing and implementing technical solutions that bridge data engineering and AI/ML requirements. You must demonstrate proficiency in building robust pipelines using Snowflake, dbt, and AWS services (S3, Glue, Redshift) that can support enterprise-scale platforms while maintaining high data quality.
Operationalizing AI/ML Models and Governance
You are expected to manage the full lifecycle of AI/ML models, including deployment, monitoring, and governance. This involves ensuring compliance with data security and privacy regulations, as well as utilizing tools like Fiddler Exchange for AI observability to maintain reliability and operational excellence.
Cross-Functional Collaboration and Stakeholder Management
Success in this role depends on your ability to act as a bridge between Data Engineering, Data Science, ML Engineering, and DevOps teams. You must be able to translate complex business requirements into technical specifications and communicate effectively with diverse stakeholders.

Likely challenges to think through

Balancing Traditional DWH with Emerging GenAI Workloads
The role demands expertise in both traditional DWH/BI (Snowflake, SQL) and modern Generative AI/LLMOps (Bedrock, RAG, vector databases). The challenge lies in integrating these disparate paradigms into a single, cohesive architecture. Prepare by articulating how you have successfully migrated or extended legacy data workflows to support modern AI agents.
Troubleshooting Complex Production Issues in Distributed Environments
Managing enterprise-scale platforms across AWS and Snowflake introduces significant complexity in debugging. You will likely face issues related to latency, data drift, or model performance. Prepare by discussing your systematic approach to root cause analysis using CloudWatch and other monitoring tools in a CI/CD-driven environment.

Practice your answers

Technical practice

Designing Scalable Data Pipelines for AI/ML

What to coverExplain your approach to building data pipelines that support enterprise-scale AI/ML platforms. Focus on how you ensure scalability, handle high-volume data ingestion using tools like AWS Glue or Snowflake, and maintain data quality throughout the lifecycle.

Integrating dbt with Snowflake for Analytics Engineering

What to coverDescribe how you utilize dbt to manage transformations within a Snowflake environment. Discuss your strategy for modularizing SQL code, managing dependencies, and implementing testing to ensure data reliability.

Implementing RAG Pipelines and Vector Databases

What to coverDiscuss the architecture of a Retrieval-Augmented Generation (RAG) pipeline. Explain how you handle data indexing, vector database selection, and the integration with AWS Bedrock to provide context-aware responses for LLMs.

Managing Data Governance and Security in Cloud Environments

What to coverDetail your experience with enforcing data governance, security, and privacy standards in AWS and Snowflake. Address how you handle sensitive data, access controls, and regulatory compliance requirements in a production setting.

Operationalizing ML Models with MLOps and LLMOps

What to coverExplain your process for deploying and monitoring models using Amazon SageMaker. Discuss how you incorporate CI/CD practices, model versioning, and observability tools like Fiddler Exchange to maintain model performance.

Troubleshooting Complex Production Issues

What to coverDescribe a scenario where you had to debug a critical failure in a data pipeline or ML model. Explain your methodology for identifying the root cause using tools like AWS CloudWatch and your steps for remediation.

Infrastructure as Code with Terraform

What to coverDiscuss how you use Terraform to provision and manage cloud infrastructure for data and AI projects. Explain the benefits of using IaC for maintaining environment consistency and auditability.

Dimensional Modeling for AI/ML Readiness

What to coverExplain how you apply dimensional modeling techniques to structure data for AI/ML consumption. Discuss the trade-offs between star schema and other formats when preparing data for feature engineering.

Behavioural practice

Cross-functional Collaboration

What to coverDescribe a time you had to align Data Science, ML Engineering, and DevOps teams on a shared technical goal. Explain how you managed conflicting priorities and ensured the final solution met all stakeholder requirements.

Translating Business Requirements into Technical Solutions

What to coverProvide an example of when you had to translate a vague business request into a concrete technical architecture. Detail the steps you took to clarify the requirements and the resulting impact on the project.

Handling Stakeholder Expectations

What to coverShare an experience where you had to manage stakeholder expectations regarding project timelines or technical limitations. Explain how you communicated the constraints and maintained a positive relationship while delivering value.

Adaptability in a Fast-Paced Environment

What to coverDescribe a situation where you had to quickly adapt to new technologies or changing project scopes. Explain how you maintained operational excellence and reliability during this transition.

Sharpen your knowledge

Topics to prepare

Enterprise AI/ML Platform Governance
Review frameworks for managing data security, privacy, and regulatory compliance within AWS-based AI environments. Exercise: Draft a checklist for ensuring PII protection and auditability when deploying models via Amazon SageMaker and Bedrock.
RAG Pipeline Architecture
Understand the conceptual flow of Retrieval-Augmented Generation (RAG) systems, including vector database integration and LLM orchestration. Exercise: Map out a high-level architecture diagram showing how data flows from Snowflake through an ETL process into a vector store for Bedrock consumption.

Skills to revise

dbt for Data Transformation
Practice building modular SQL transformations and managing data quality tests within dbt. Exercise: Create a dbt project structure that implements incremental loading for a large-scale Snowflake dataset, including automated tests for null values and uniqueness.
AWS SageMaker and Bedrock Integration
Gain hands-on experience deploying and monitoring models using AWS native tools. Exercise: Write a Python script using the Boto3 library to invoke a foundation model via Amazon Bedrock and log the performance metrics to CloudWatch.
Infrastructure as Code (Terraform)
Develop skills in automating cloud infrastructure provisioning. Exercise: Write a Terraform configuration file to deploy an S3 bucket and an AWS Glue job, ensuring the setup follows standard security best practices.

Your preparation checklist

Map your experience to enterprise-scale AI/ML platforms
Prepare a project walkthrough detailing how you have supported large-scale AI/ML platforms. Focus on your specific contributions to data pipelines, model deployment, and monitoring using AWS services like Sagemaker, Glue, and Redshift.
Demonstrate expertise in Snowflake and dbt integration
Be ready to discuss your approach to dimensional modeling and ETL/ELT processes within Snowflake. Explain how you have utilized dbt to manage data transformations and ensure high data quality in complex production environments.
Prepare evidence of GenAI and LLMOps implementation
If you have experience with RAG pipelines, vector databases, or AWS Bedrock, document the architecture you designed. Explain how you handled data security, privacy, and regulatory requirements when implementing these generative AI solutions.
Articulate your approach to data governance and security
Review your experience managing data governance, security, and privacy in a cloud environment. Prepare to explain how you have navigated regulatory requirements and ensured compliance while maintaining operational excellence.
Formulate questions regarding cross-functional collaboration
Prepare thoughtful questions for the interviewer about how the Data Engineering team collaborates with Data Science, ML Engineering, and DevOps teams at NTT DATA to ensure reliable and scalable AI/ML solutions.

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