Senior Data Engineer Interview Questions

2,611 senior data engineer interview questions shared by candidates

Coder pad had 4 questions. 1. React - 400 points - Todo List 2. Javascript - 300 points - finding maximum lucky children that will get lucky money, given money and number of children. 3. Problem solving (any language) - 300 points - Processing array of strings to construct object and sort them. 4. Problem solving (any language) - 300 points - Decoding the string with given list of words. I was asked to find the subset of an array of strings and return the maximum length of the subset which satisfies a said condition
Feb 19, 2025

Coder pad had 4 questions. 1. React - 400 points - Todo List 2. Javascript - 300 points - finding maximum lucky children that will get lucky money, given money and number of children. 3. Problem solving (any language) - 300 points - Processing array of strings to construct object and sort them. 4. Problem solving (any language) - 300 points - Decoding the string with given list of words. I was asked to find the subset of an array of strings and return the maximum length of the subset which satisfies a said condition

A. Core Data Engineering Concepts SQL (joins, window functions, performance tuning) Data Modeling (star vs snowflake, normalization) ETL/ELT pipelines (batch vs streaming, orchestration tools like Airflow) B. Apache Spark / PySpark Catalyst Optimizer & Tungsten Narrow vs Wide transformations Joins (broadcast, sort-merge), Skew handling AQE (Adaptive Query Execution) Partitioning, Predicate Pushdown Execution Plan (DAG → Stage → Tasks) Spark UI and Job Debugging SCD Type 2 Implementation in PySpark C. AWS S3, Glue, Athena, Lambda, EMR, Redshift Event-driven design (S3 → EventBridge → Lambda) Security: IAM roles, bucket policies, encryption CI/CD in AWS (CodePipeline, CloudFormation) D. Python Writing modular, reusable code Working with Pandas, Boto3 (for AWS interaction) Exception handling, logging Lambda functions and decorators E. Kafka / Streaming Kafka topic partitioning, consumer groups Offset management Integration with Spark Structured Streaming
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Senior Data Engineer

Interviewed at EPAM Systems

4
Jul 21, 2025

A. Core Data Engineering Concepts SQL (joins, window functions, performance tuning) Data Modeling (star vs snowflake, normalization) ETL/ELT pipelines (batch vs streaming, orchestration tools like Airflow) B. Apache Spark / PySpark Catalyst Optimizer & Tungsten Narrow vs Wide transformations Joins (broadcast, sort-merge), Skew handling AQE (Adaptive Query Execution) Partitioning, Predicate Pushdown Execution Plan (DAG → Stage → Tasks) Spark UI and Job Debugging SCD Type 2 Implementation in PySpark C. AWS S3, Glue, Athena, Lambda, EMR, Redshift Event-driven design (S3 → EventBridge → Lambda) Security: IAM roles, bucket policies, encryption CI/CD in AWS (CodePipeline, CloudFormation) D. Python Writing modular, reusable code Working with Pandas, Boto3 (for AWS interaction) Exception handling, logging Lambda functions and decorators E. Kafka / Streaming Kafka topic partitioning, consumer groups Offset management Integration with Spark Structured Streaming

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