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Python interview questions for data engineers

Python and data structures problems written for data engineering interviews. Parse and clean records, group and deduplicate with dictionaries, stream with generators, and solve the DSA patterns (sliding windows, heaps, graphs) that coding rounds still ask for.

120 Python + DSA problems, 18 of them free. They run in your browser against the same warehouse tables used in Learn. All problems · Python + DSA filter · Theory questions · Learn tracks

Python the way data engineers use it

Most problems start from records: a list of events, a log file, a batch of API rows. You clean, group, join, and summarize them with plain Python, the same work you would do before data reaches a warehouse.

DSA without the puzzle feel

The algorithms round is still common. Here the classic patterns are framed around data tasks, so practicing a heap or a sliding window also teaches you where it shows up in a pipeline.

Beginner (40)

  • Flatten Nested Lists
  • First Occurrence Map
  • Merge Two Sorted Lists
  • Word Frequency Counter
  • Collapse Consecutive Duplicates
  • Top K Words
  • Remove Duplicates Preserving Order
  • Find Intersection
  • Two Sum
  • Find Missing Number
  • Valid Parentheses
  • Longest Consecutive Sequence
  • Group Anagrams
  • Product Except Self
  • Max Subarray Sum
  • Contains Duplicate
  • Valid Anagram
  • Best Time Buy Sell
  • 3Sum
  • Container With Most Water
  • Find All Duplicates
  • Find Disappeared
  • Majority Element
  • Find Duplicate Number
  • Most common word in a support ticket
  • Second largest order total
  • Shared tags between two campaigns
  • Rotate a shift schedule
  • Reverse words in a log message
  • Coordinates as immutable records
  • Running sum of daily signups
  • Validate a discount code format
  • Sort products by price then name
  • Even totals from a mixed order list
  • Parse a delimited inventory line
  • Process print jobs in arrival order
  • Time a function's execution
  • Counter for warehouse zone visits
  • Compact a run-length encoded event log
  • Expand a run-length encoded shift log

Intermediate (53)

  • Trapping Rain Water
  • Valid Palindrome
  • Longest Substring No Repeat
  • Min Window Substring
  • Longest Repeating Char Replace
  • Permutation in String
  • Find All Anagrams
  • Sliding Window Max
  • Longest Substring K Distinct
  • Subarray Sum Equals K
  • Find Anagrams Window
  • Merge Intervals
  • Insert Interval
  • Non-Overlapping Intervals
  • Meeting Rooms
  • Meeting Rooms II
  • Reverse Linked List
  • Merge Two Sorted Lists
  • Linked List Cycle
  • Middle of Linked List
  • Remove Nth From End
  • Reorder List
  • Copy Random List
  • Stack
  • Queue
  • Min Stack
  • Eval RPN
  • Daily Temperatures
  • Normalize a batch of phone numbers
  • Word frequency ignoring stop words
  • Deduplicate customer records by fuzzy key
  • Group orders by customer and status
  • Longest streak of active days
  • Best 3-day sales window
  • First non-repeating character in a request id
  • Match opening and closing tags
  • Parse a fixed-width mainframe extract
  • Merge two JSON-like config dicts with overrides
  • Extract all email addresses from a nested payload
  • Batch a stream with a size limit
  • Lazily read and filter a large log iterator
  • Safely parse a batch of numeric strings
  • Retry a flaky operation with a cap
  • Model an inventory item with validation
  • Memoize an expensive lookup function
  • Top-K most active accounts
  • Anagram grouping of product SKUs
  • Validate a nested order schema
  • Flatten a directory listing into paths
  • Find the pivot index of a sales list
  • Two-account transfer matching
  • Chunk transactions into daily batches by cutoff time
  • Custom sort by multiple derived keys

Advanced (27)

  • Invert Binary Tree
  • Max Depth
  • Same Tree
  • Subtree
  • LCA BST
  • Level Order
  • Validate BST
  • Number of Islands
  • Clone Graph
  • Course Schedule
  • Connected Components
  • Graph Valid Tree
  • MedianFinder
  • Top K Frequent
  • Merge K Lists
  • Coin Change
  • House Robber
  • Climbing Stairs
  • A simple LRU-style access cache
  • Interval merge for maintenance windows
  • Balanced team assignment by workload
  • Weighted round-robin task scheduler
  • Sliding window unique visitor count
  • Detect a cycle in a task dependency map
  • Topological build order for services
  • Context manager for a scoped transaction log
  • Diff two versions of a product catalog

Common questions

Do I need Python installed?
No. Code runs in your browser tab through Pyodide, a full Python runtime compiled for the web.
Should I still use LeetCode for DSA?
It is a good complement for pure algorithms practice. Lakebench focuses on the Python and DSA questions data engineering interviews tend to ask, alongside SQL and PySpark.
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