Walmart Data Engineer Interview Question — Data Engineer III

2026-04-26 · Source: Data Engineering on Medium · Field: Technology & Digital — Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, quick

Summary

A common Walmart Data Engineer III interview question involves transforming a given table of characters, which forms a string, into a palindrome. The task requires appending the reverse of the original string to itself. The core idea is to preserve the original character order, reverse the order of the same characters, and then combine both sequences. The proposed SQL solution utilizes `ROW_NUMBER()` to maintain the original ordering, reverses the sequence by ordering in descending order, and then appends the two parts using `UNION ALL`. This approach ensures the resulting string is a perfect palindrome, as demonstrated by the expected output example.

Key takeaway

For Data Engineers preparing for interviews, understanding how to manipulate string data to form palindromes is crucial. You should practice using window functions like `ROW_NUMBER()` to maintain order and `UNION ALL` to combine datasets effectively. This problem highlights fundamental SQL and PySpark skills essential for data transformation tasks.

Key insights

Transforming a string into a palindrome involves appending its reverse to the original string.

Principles

• Preserve original data order.
• Reverse data order for appending.

Method

Use `ROW_NUMBER()` for ordering, reverse by sorting in descending order, and combine sequences with `UNION ALL` to form a palindrome.

In practice

• Apply `ROW_NUMBER()` for sequence integrity.
• Use `UNION ALL` for combining datasets.

Topics

• Data Engineering Interview
• SQL Data Manipulation
• Palindrome Generation
• ROW_NUMBER()
• UNION ALL

Best for: Data Engineer, Machine Learning Engineer, AI Student

Related on AIssential

• From YouTube SEO to Data Engineering: My First Six Months of Learning — The transition to Data Engineering requires foundational skills, project-based learning, and a deep understanding of system architecture.
• Finding Duplicates in Tabular Data with Jupyter and Prodigy — Effective data deduplication for "near duplicates" requires a human-in-the-loop, supported by tools that reduce candidate pairs and customize the labeling experience.
• Window Functions: ROW_NUMBER vs RANK vs DENSE_RANK (Three Functions, One Confusion — Fixed in 5… — SQL window functions `ROW_NUMBER`, `RANK`, and `DENSE_RANK` differ primarily in how they handle ties and number skipping.
• Creating Synthetic Data In Snowflake Part II — Snowflake functions like `Seq`, `Uniform`, `Randstr`, `Normal`, and `Get` enable robust synthetic data generation.
• 3 Pandas Tricks for Data Cleaning & Preparation — Idiomatic Pandas patterns significantly boost data cleaning speed and memory efficiency by avoiding common performance pitfalls.
• Escaping the SQL Jungle — Unmanaged ELT transformations lead to a "SQL jungle"; a structured transformation layer restores order and consistency.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Data Engineering on Medium.