Round 1: Technical (AWS Data Engineering Focus)

✅ Introduction

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

📍 Follow-up: Questions related to your projects.

✅ AWS Concepts and Practices

📍 What is a stored procedure, and how is it used in Amazon RDS or Redshift?

📍 How would you remove duplicate values in your data using AWS Glue or Lambda?

📍 Explain how you would use Amazon EMR to run a Spark job on large datasets stored in S3.

📍 How can you optimize Spark jobs in AWS to ensure that they run efficiently and cost-effectively?

📍 What are some key considerations when choosing between Spark on AWS EMR versus using AWS Glue for ETL workloads?

📍 How would you handle and monitor Spark jobs in AWS EMR or Glue to detect issues like data skew or job failures?

✅ AWS Services Overview

📍 Questions related to AWS services used for data engineering such as S3, Redshift, Glue, EMR, Lambda, and Kinesis.

Round 2: Advanced Technical (Spark and AWS Integration)

✅ Introduction

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

📍 Follow-up: "Questions related to your projects.

✅ Technical Questions

📍 What is the significance of partitioning in Spark, and how do you manage partitioning in AWS (e.g., in S3 or Redshift)?

📍 How would you write Spark code to join large datasets that are stored in different S3 buckets using AWS Glue?

📍 Describe the architecture for building a data lake using AWS S3 and how you would use Spark to process the data.

📍 Explain the difference between RDDs, DataFrames, and Datasets in Spark. Which one would you prefer in different data engineering scenarios?

📍 How do you perform data transformations using Spark SQL, and what are the benefits of using Spark SQL over traditional Spark RDDs?

✅ Optimization and Best Practices

📍 What are the best practices for optimizing Spark performance when working with large datasets in AWS?

📍 How would you set up continuous integration and deployment (CI/CD) for a Spark-based ETL pipeline in AWS?

📍 Scenario-based questions on handling schema changes and data evolution in a Spark-based pipeline in AWS Glue or EMR.

📍 Questions related to Spark optimizations: What are they, and when should they be used?

✅ Coding Challenge

📍 Write a Python function to flatten a nested list (a list of lists) into a single list.

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?