Question 1

What Python topics are tested in data engineering interviews?

Accepted Answer

Data engineering Python rounds focus on: PySpark DataFrame operations, pandas data manipulation, file I/O and JSON/CSV parsing, API integrations, basic algorithms and data structures, error handling patterns, and writing Airflow DAGs or pipeline code.

Question 2

Is Python coding for data engineers easier than for software engineers?

Accepted Answer

Generally yes. Data engineering Python rounds rarely include LeetCode-hard algorithm problems. Instead, they test practical data manipulation, PySpark operations, and pipeline-oriented code. However, some FAANG companies still include a standard coding round.

Question 3

Should I learn PySpark or pandas for interviews?

Accepted Answer

Learn both. PySpark is tested for distributed processing scenarios (large datasets, Spark cluster operations). Pandas is tested for smaller-scale data manipulation and analysis. Most interviewers expect fluency in both, with PySpark being more critical for senior roles.

Question 4

What are traits in Scala, and how are they different from classes?

Accepted Answer

**Traits**: Interface-like constructs that can define abstract and concrete methods/fields. Support multiple inheritance of type. Mixed in via `with`.

**Classes**: Define objects with state and behavior. Single inheritance; one superclass.

**Key Differences**: Traits enable composition; classes define core logic. Traits can be partially implemented; classes hold primary behavior....

Question 5

Write a Python function to check if a string is a palindrome.

Accepted Answer

**Architectural logic**: A palindrome reads the same forwards and backwards. We need to normalize (case, non-alphanumeric) and compare. **Approach 1 (string ops)**: `cleaned = "".join(c.lower() for c in s if c.isalnum()); return cleaned == cleaned[::-1]`—O(n) time, O(n) space. **Approach 2 (two-pointer)**: Compare from both ends; O(n) time, O(1) space if not normalizing....

Question 6

What is the difference between a list and a tuple in Python?

Accepted Answer

List: Mutable, []; tuple: immutable, (). Why it matters: Mutability drives use—lists for collections that change; tuples for fixed data, dict keys (hashable), multiple return values. Performance: Tuples are slightly faster (less overhead, fixed size). Hashability: Tuples can be dict keys/set members; lists cannot. In data pipelines: Tuples for schema-like rows (column names); lists for buffers, accumulators....

Question 7

Explain the difference between shallow copy and deep copy in Python.

Accepted Answer

Shallow (copy.copy()): New top-level object; nested objects are references. Nested mutations affect original. Deep (copy.deepcopy()): Recursive copy; fully independent. Why it matters: Shallow is O(n) for top level only; deep is O(n) for entire structure—can be slow for large nested dicts. Use shallow when: No nested mutables or shared refs OK. Use deep when: Need full isolation (e.g., config that will be modified)....

Question 8

Write a Python function to find the first non-repeating character in a string.

Accepted Answer

Approach: Two-pass—count chars, then find first with count 1. Code: def first_non_repeating(s): counts = {}; [counts.update({c: counts.get(c, 0) + 1}) for c in s]; return next((c for c in s if counts[c] == 1), None). Or: from collections import Counter; counts = Counter(s); return next((c for c in s if counts[c] == 1), None). Complexity: O(n) time, O(k) space. Why: Single pass can't know if char is unique until full scan....

Python/Coding Data Engineer Interview Questions

Reading isn't practice. Get AI feedback on your answers.

Python/Coding Interview Preparation FAQ

Python/Coding Data Engineer Interview Questions

Reading isn't practice. Get AI feedback on your answers.

Python/Coding Interview Preparation FAQ