Round 1: Technical (AWS Data Engineering and Spark)

✅ Introduction

📍 Tell me about yourself and any recent projects you have been a part of.

📍 Questions related to your projects.

✅ Technical Questions

📍 How would you fetch data from an API, and how would you build a pipeline to process it using AWS services like Lambda, API Gateway, and S3?

📍 How would you migrate data from an on-premises SQL database to AWS?

📍 How does the AWS Glue service connect to an on-premises SQL Server to extract data?

📍 How would you create a pipeline in AWS Data Pipeline to copy only recently modified files from S3 buckets to an output bucket in the same S3?

✅ Scenario-Based Questions

📍 Explain how you would implement partitioning and bucketing in Spark for data stored in S3 to optimize query performance.

📍 How would you design a data pipeline for near real-time data ingestion and processing from various sources (e.g., S3, Kinesis) to Redshift using Spark?

📍 Describe how you would use AWS Glue for orchestration and scheduling of Spark jobs.

📍 How do you handle incremental updates in a data lake architecture using AWS services and Spark?

📍 What is your approach to monitoring and logging Spark jobs in AWS, and how do you track performance issues?

✅ AWS Service-Specific Questions

📍Scenario-based questions related to AWS Data Pipeline, Glue, or Lambda.

Round 2: Team Fitment and Company Understanding (15 Minutes)

✅ Company Knowledge

📍 What do you know about EPAM?

✅ Team Fitment

📍 Questions to assess your compatibility with the team and work culture.

✅ Reason for Change

📍 What is your reason for seeking a job change?

Round 3: HR Interview

✅ Experience and Projects

📍 Discuss your experience and recent projects.

📍 Resume-specific questions related to your skills and achievements.

✅ Role Expectations and Fit

📍 What are you expecting in your next job role?

📍 How soon can you join the company, and what is your preferred location?