Updated for 2026
This article was originally written in 2022 and has been revised to reflect modern Data Engineering interview expectations in 2026, including cloud-native pipelines, SQL-heavy roles, and AI-ready data systems.
In my previous post How to Prepare for Data Engineer Interviews (2026), I discussed a structured approach to interview preparation.
In this post, I cover frequently asked basic Data Engineering interview questions with brief answers.
👉 These questions are typically asked in early interview rounds, where interviewers want to assess:
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Conceptual clarity
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Practical understanding
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Ability to reason, not memorize
You are not expected to go deep in these rounds — clarity matters more than depth.
A. Programming (Python for Data Engineers)
1. What is a static method in Python?
A static method is bound to a class rather than an instance. It does not access instance variables and can be called using the class name. Static methods are commonly used for utility logic in data pipelines.
2. What is a decorator in Python?
Decorators add additional functionality to functions without modifying their definition. In Data Engineering, decorators are often used for logging, retries, validation, and monitoring pipeline functions.
3. What is a dictionary in Python?
A dictionary is a key–value data structure based on hash tables. It provides O(1) average time complexity for lookup, insertion, and deletion, making it useful for aggregations, lookups, and joins in data processing logic.
4. Difference between list and tuple?
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Lists are mutable, tuples are immutable
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Tuples are faster and memory-efficient
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Tuples are often used for fixed schema data
5. Difference between arrays and lists?
Lists can store heterogeneous elements, while arrays store homogeneous elements. Arrays are memory-efficient and used in numerical processing, whereas lists are more flexible.
6. What are NamedTuple and DefaultDict?
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NamedTuple provides tuple-like objects with named fields
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DefaultDict returns a default value instead of raising
KeyError, useful for aggregations and counters
7. What are generator functions?
Generators use yield instead of return and generate values lazily. They are memory-efficient and widely used for processing large datasets or streaming data.
8. How is Python used in Data Engineering (2026)?
Python is used for:
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Data ingestion
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Orchestration logic
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Spark/Beam jobs
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API integration
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Data validation and testing
B. Data Structures & Algorithms (Interview Level)
1. What is Binary Search?
Binary search works on sorted data by repeatedly dividing the search space in half.
Time complexity: O(log n).
2. Time complexity of Merge Sort and Quick Sort?
Both have average time complexity of O(n log n). Merge Sort guarantees this in worst case, while Quick Sort may degrade to O(n²).
3. What are BFS and DFS?
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BFS explores level by level using a queue
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DFS explores depth-first using a stack or recursion
Used in dependency graphs and workflow traversal.
4. What is memoization?
Memoization caches results of expensive function calls to avoid recomputation. It is commonly used in optimization and dynamic programming.
5. What is Dynamic Programming?
Dynamic Programming solves problems by breaking them into overlapping subproblems and storing intermediate results.
C. Distributed Systems & Databases
1. What is the CAP Theorem?
CAP states that in a distributed system, you can guarantee only two of the three:
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Consistency
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Availability
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Partition tolerance
Modern systems choose trade-offs based on use case.
2. What is sharding?
Sharding distributes data across multiple machines to handle large datasets and scale horizontally.
3. What is master–slave architecture?
A master node handles writes, and slave nodes handle reads. This improves scalability but introduces replication lag.
4. What is a NoSQL database?
NoSQL databases are schema-flexible and optimized for scale and availability. They differ from relational databases in consistency models and query capabilities.
5. What are columnar databases?
Columnar databases store data by columns instead of rows, enabling fast analytical queries. They are widely used in OLAP workloads.
6. When should you use denormalized tables?
Denormalization improves query performance by reducing joins at the cost of storage and redundancy. Common in analytics systems.
D. Data Modeling
1. What is a star schema?
A star schema consists of:
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One fact table
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Multiple dimension tables
Optimized for analytical queries.
2. What is a snowflake schema?
A normalized version of star schema where dimensions are split into multiple related tables.
3. When to use star vs snowflake schema?
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Star schema → performance-focused
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Snowflake schema → storage and data integrity
4. What are fact and dimension tables?
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Fact tables store measurable metrics
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Dimension tables store descriptive attributes
E. Data Engineering & SQL (2026 Focus)
1. How would you design an end-to-end data pipeline?
Typical flow:
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Ingest raw data into a data lake
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Transform and validate data
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Load into analytics warehouse
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Monitor, retry, and backfill failures
Reliability and idempotency are critical.
2. How do you process huge volumes of data?
Using distributed processing frameworks that parallelize computation across multiple machines and scale automatically.
3. Difference between GROUP BY and PARTITION BY?
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GROUP BY reduces rows
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PARTITION BY keeps row count intact and adds windowed aggregates
4. Explain SQL joins.
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INNER JOIN
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LEFT JOIN
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RIGHT JOIN
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FULL JOIN
Understanding join behavior is critical to avoid duplication bugs.
5. How do you handle late-arriving data?
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Use watermarks
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Allow overlap in incremental loads
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Deduplicate downstream
6. How do you ensure data quality?
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Row count checks
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Schema validation
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Null checks
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Freshness monitoring
7. How is SQL used in modern data stacks?
SQL is used for:
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Transformations (ELT)
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Analytics
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Feature preparation
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AI data pipelines
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