Easy-level general questions from real data engineering interviews.
These easy general questions are selected from real interviews at top companies. Each question includes a detailed expert answer and pro tip to help you nail your interview. This set leans toward fundamentals — 58 easy, 0 medium, and 0 hard questions. Recurring themes are spark, airflow, and python — these patterns appear most often in real interviews and reward the deepest preparation. These questions have been reported across 37 companies including Capco and EPAM. Average answer is around 2 minutes of reading — plan roughly 2 hours to work through the full set thoughtfully.
This collection contains 58 curated questions: 58 easy. There's a strong foundation of fundamentals-focused questions — ideal for building confidence before tackling advanced topics.
The most frequently tested areas in this set are spark (7), airflow (5), python (2), bigquery (2), etl (2), and snowflake (2). Focusing on these topics will give you the highest return on your preparation time.
Start with the easy questions to warm up and solidify fundamentals. For each question, try answering before revealing the solution. Use our AI Mock Interview to simulate real interview conditions and get instant feedback on your responses.
Agile in project management?
Basic logical or analytical puzzle
Can you share an example of a project you worked on that had a significant impact on your organization?
Combine records by name with concatenated course values
Daily Data Volume - quantify
Data access strategy for clients
Data masking scenarios for secure data handling
Deadlock Prevention - how deadlocks occur and how to prevent them
Deadlock: Definition and necessary conditions
Describe a project where you implemented a data quality framework.
Git Bash Commands
Git: Copying a Branch
HTTP vs HTTPS Protocol
How do bucket policies handle the Principal element for cross-account roles?
How do you check the memory of your laptop using Linux commands?
How do you handle fluctuations in active users?
How do you handle passing parameters between notebooks?
How do you keep up with learning? Have you attended any conferences or engaged in other learning activities?
How do you keep up with the latest trends or tools in data engineering?
How do you manage competing priorities in an Agile environment?
How do you move files in DBFS?
How do you prioritize competing demands in a high-pressure environment?
How do you run one notebook in another notebook?
How do you secure API requests in this setup?
How do you secure the connection for sensitive data transfers?
How do you see files before update (history records/versioning)?
How does Versioning impact replication behavior?
How does resource allocation adjust when a job experiences a sudden load increase?
How is Oozie called?
How would you ensure data consistency between the source and destination regions?
How would you implement a data quality framework using AWS services?
Share good and bad experiences with past employers.
Shell: change permissions?
Shell: command to check processes running in the background?
Walk me through your resume.
What I understand about the profile
What are the implications of enabling encryption at rest on storage performance?
What are you seeking in your next role that your current position does not offer?
What are your expectations for this role?
What are your expectations from the next job role?
What are your reasons for wanting a job change?
What aspects of our business model excite you the most?
What attracts you to working at Moonfare?
What benefits or perks are most important to you?
What do I know about Nasdaq?
What do you think about Data uncertainty?
What do you think differentiates EPAM from other consulting firms in the data engineering space?
What does social responsibility mean to you in the context of your work?
What excites you about working at Google?
What is Multiline option in JSON?
What is ParDo and Map?
What is a Foreign Key?
What is currying in Scala?
What is the P2 ticket SLA?
What is the difference between SAFE_CAST() and CAST()?
What is the impact of multipart uploads on lifecycle policies?
What is your cluster configuration?
What role does data lineage play in your current project?
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