Walmart SQL Interview Questions 2025: Complete Prep & Strategy Guide

Walmart operates one of the world’s largest supply chains and retail data ecosystems—tracking billions of transactions across stores, fulfillment centers, and digital platforms. That scale demands analysts and engineers who can write SQL not just correctly, but efficiently, clearly, and with business context.

Whether you’re interviewing for a Walmart Data Analyst, Data Engineer, or BI Developer role, SQL will likely be the first and most important skill tested. This guide breaks down the Walmart SQL interview questions you’ll face in 2025, walking through question types, difficulty tiers, mock challenges, and strategy tips—all mapped to the Walmart interview process from entry-level to senior.

Why Walmart Cares About SQL Fluency

SQL is Walmart’s backbone for data access—from identifying shelf gaps in Arkansas to aggregating customer retention data in India. It powers dashboards for executives, anomaly alerts for supply chain managers, and experimentation reports for eCommerce teams.

Unlike startups where datasets are small enough to handle in-memory, Walmart’s scale demands query fluency under real-world constraints: timeouts, data duplication, late-arriving facts, and cross-regional consistency. That’s why SQL mastery often makes or breaks your performance in the Walmart data analyst or Walmart data engineer interview.

Walmart SQL interview questions go beyond textbook joins—they reflect the messy, massive, and mission-critical nature of Walmart’s data. They’re designed to test how well you understand scale, accuracy, and real-time business needs.

Expect to face three major types:

Conceptual SQL Questions

These focus on the why behind the syntax—like the difference between INNER and LEFT JOIN, when to use HAVING, or how NULL values affect logic. They typically appear in early analyst rounds or as warmups during technical phone screens.

Applied SQL Challenges

The majority of questions fall into this bucket. You’ll be asked to write SQL against mock order, sales, or inventory tables—e.g., “Find the top-selling product in each region by revenue over the past month.” These questions test logic, grouping, date filtering, and clean business reasoning.

Edge-Case + Optimization Scenarios

These show up more in senior rounds. You’ll deal with large tables, inefficient joins, or query timeouts. Sometimes you’re asked to debug or refactor a poor query. You’ll need to balance accuracy with compute efficiency—especially in Redshift, which Walmart frequently uses.

Basic Walmart SQL Interview Questions (0–2 Years)

At the entry level, Walmart SQL interview questions focus on foundational concepts that test your ability to retrieve, filter, and summarize relational data. These questions commonly appear in analyst interviews, new grad roles, and early technical screens. Expect to work with 2–3 tables at a time and translate a business request (like “find top-selling categories”) into SQL logic.

The following section contains up to 10 real-world beginner prompts with sample answers, focusing on:

• Basic filtering and WHERE logic
• Joins (INNER, LEFT)
• Aggregations (SUM, COUNT, AVG)
• GROUP BY and sorting
• Intro to CASE WHEN syntax

You’ll typically be asked to use simple joins, conditional filters, and GROUP BY clauses to compute aggregates. Bonus points go to candidates who write clean, readable queries—and who can explain why they chose a particular approach. While these questions may seem straightforward, they’re designed to catch careless mistakes, such as missing edge cases or incorrect join logic.

1. Which neighborhoods have no registered users in the system?

   LEFT JOIN neighborhoods n ON n.id = u.neighborhood_id and filter WHERE u.neighborhood_id IS NULL. This anti-join pattern checks your grasp of NULL logic and is a staple for identifying coverage gaps in Walmart’s delivery zones.

2. Which products have never appeared in a completed order?

   Anti-join the products table to the order_items fact table on product_id, keeping WHERE order_items.product_id IS NULL. A clean answer proves you can find “orphans” that may need clearance or de-listing.

3. What was the total number of orders and total GMV per store yesterday?

   Filter orders to order_date = CURRENT_DATE - 1, then GROUP BY store_id and compute COUNT(*) AS order_cnt, SUM(order_total) AS gmv. This confirms basic filtering, grouping, and date arithmetic.

4. List the top five selling product categories by unit volume last month.

   Join order_items → products → categories, restrict to last-month dates, SUM(quantity) per category_name, order desc, LIMIT 5. Demonstrates multi-table joins plus aggregate ranking.

5. For each customer, how many distinct stores have they shopped at?

   SELECT customer_id, COUNT(DISTINCT store_id) AS store_visits FROM orders GROUP BY customer_id. Shows comfort with COUNT(DISTINCT …) and basic customer segmentation.

6. Compute the average basket size (items per order) for online vs in-store channels.

   Use AVG(item_cnt) where item_cnt is COUNT(*) OVER (PARTITION BY order_id) in a sub-query, then GROUP BY channel. This question introduces window functions in a beginner-friendly way and checks whether you can bucket by a categorical field.

7. Which five associates logged the most overtime hours last week?

   Filter employee_shifts to the last ISO week, calculate SUM(overtime_hours) per associate_id, order desc, LIMIT 5. The twist is using a date filter function like DATE_TRUNC('week', shift_date).

8. Find the percentage of total sales generated by “Rollback” (promotion) items in Q1 of the current year.

   Compute SUM(case when promo_flag = 'Rollback' then sales else 0 end) / SUM(sales) for dates between Jan 1 and Mar 31. The interviewer checks your CASE WHEN syntax and ratio calculations.

9. Return each day’s total online orders alongside the seven-day rolling average.

   Aggregate daily COUNT(*) AS orders, then apply AVG(orders) OVER (ORDER BY order_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) as seven_day_avg. This exposes candidates to basic window-frame definitions without getting too advanced.

10. Which suppliers delivered fewer than three distinct SKUs to any Walmart distribution center last quarter?

    Join deliveries and products, filter to last quarter, count distinct sku per supplier_id, dc_id, HAVING COUNT(DISTINCT sku) < 3. Verifies understanding of multi-column grouping and conditional thresholds.

Intermediate Walmart SQL Interview Questions (3–5 Years)

Once you move into mid-level roles—especially for Walmart data engineer or data scientist positions—the interview becomes less about correctness and more about query design and performance. Questions at this level often require 3–5 steps of logic and must be written with an eye toward maintainability.

Expect interviewers to probe your comfort with larger datasets, ask you to explain time complexity or execution plans, and quiz you on design trade-offs. Often, you’ll be working with a simulated Walmart dataset (orders, SKUs, stores, returns) and asked to uncover business insights that require joining fact and dimension tables.

Subtheme: Query Optimization & Indexing

Questions under this subtheme revolve around improving performance in distributed environments, such as Redshift or Snowflake. You may be asked to:

• Rewrite a slow query using EXISTS instead of a subquery
• Analyze an EXPLAIN plan and identify bottlenecks
• Apply best practices for indexing or partitioning

This segment also often features variations of “how would you refactor this query for a 100M row table?”

1. How would you pull the last transaction of each day—including its ID, timestamp, and amount—while keeping the query fast on a very large bank_transactions table?

   A window-function solution (ROW_NUMBER() OVER …) surfaces the daily max created_at; the accompanying discussion revolves around clustering or sort keys on (created_at DESC) so Redshift can satisfy the window with a range-scan instead of a spill. Interviewers want to hear how you’d verify this in an EXPLAIN plan and what composite index you’d add if the table lived in MySQL.

2. Which customers placed more than three transactions in both 2019 and 2020—and how would you keep that query efficient?

   One approach uses CTEs for each year with COUNT(*) > 3 and an INTERSECT to find the overlap. Expect follow-ups on why an EXISTS filter with a composite (customer_id, txn_date) index might outperform the two-CTE pattern on a 500 GB partitioned fact table.

3. A merchandising analyst needs the top 10 SKUs by return-rate for the previous quarter. The returns fact table holds 500 million rows and is partitioned by return_date. How would you author—and index—this query so it finishes under 30 seconds in Redshift?

   Filter partitions with return_date BETWEEN quarter_start AND quarter_end, join to orders on order_id, compute SUM(returned_qty) / SUM(sold_qty) per sku_id, and order by that ratio DESC LIMIT 10. Cluster keys on (sku_id, return_date) ensure the join and aggregate step stay data-local. You’d also pre-aggregate daily SKU return metrics in an ETL job to avoid scanning raw facts.

4. A teammate wrote a correlated sub-query to flag orders that include at least one out-of-stock item. It times out on the 1.2 B-row order_items table. Show how you’d rewrite it using EXISTS (or LEFT JOIN … IS NULL) and which composite index or sort key you’d add to cut execution time.

   Switching to EXISTS lets the planner stop at the first matching row, while an index on (order_id, stock_status)—or a Redshift sort key on the same columns—eliminates a full-table scan. The interviewer wants to hear you cite the early-exit benefit and how EXPLAIN verifies fewer blocks read.

5. Daily active‐user counts on the session_events fact table have slowed from 45 s to 6 min as data hit 800 GB. Walk through how you’d read the EXPLAIN plan, identify the bottleneck, and refactor the query (or table design) so DAU refreshes in < 1 min.

   Expect to suggest a date/key dist-style, late-materialized aggregates, Bloom filters, and possibly a materialized view keyed by event_date, user_id to pre-deduplicate sessions.

Subtheme: Data Modeling & Aggregations

Here, interviewers test your ability to work with semi-complex schemas involving nested aggregations, CTEs, or calculated metrics. You might:

• Build cohort analyses using ROW_NUMBER()
• Construct rolling averages or YOY comparisons
• Normalize or denormalize mock table structures on the fly

Many of these questions also test your ability to reason about NULLs, type casting, and edge cases in grouping logic.

1. Do Walmart customers tend to ship more orders to their primary address or to other addresses? Return the percentage of “home” orders.

   Join transactions to users, then aggregate with a CASE WHEN ship_addr_id = primary_addr_id flag. Candidates must reason about NULLs (e.g., guests), choose the right denominator, and justify why a derived boolean column might belong in a consumer-fact table for recurring analyses.

2. After each restock, what is the running total of sales for every product?

   First locate the latest restock_date that is ≤ each sale_date via a max-date join, then compute SUM(price) OVER (PARTITION BY product_id, restock_cycle ORDER BY sale_date) as the cumulative figure. Interviewers probe your ability to model “gaps-and-islands” (restock cycles) and craft a window frame that resets correctly—an everyday need for inventory and replenishment analytics at Walmart scale.

3. Compute a 12-month rolling average of net sales for every product category. Your answer should include category_id, month, and rolling_avg_sales.

   Aggregate monthly sales in a CTE, then apply AVG(month_sales) OVER (PARTITION BY category_id ORDER BY month ROWS BETWEEN 11 PRECEDING AND CURRENT ROW). Discuss NULL-padding missing months and why clustering the staging table on (category_id, month) speeds the window pass.

4. Build a first-purchase cohort table that shows, for each signup month, what percentage of users make a repeat purchase in months 1, 2, and 3 after their first order. Explain any CTEs, row-number tricks, or calendar tables you’d use.

   ROW_NUMBER() isolates the first order per user; joining back on user/month pairs allows a pivot that counts distinct repeat purchasers. Interviewers look for clean cohort framing and edge-case handling (e.g., partial months).

5. Calculate year-over-year (YoY) growth in foot-traffic for every Walmart Supercenter, rounded to two decimals.

   Derive daily visits from store_visits, roll up to calendar year, then use LAG(total_visits) OVER (PARTITION BY store_id ORDER BY year) to compute (curr – prev)/prev. Emphasize how NULLs (new stores) and divide-by-zero should be treated.

6. For each product, determine the average days-on-hand inventory by dividing current on-hand units by the trailing-30-day sales rate. What window and grouping logic gets you there, and where would you persist the intermediate daily sales rate?

   Calculate 30-day rolling sales via SUM(qty) OVER (PARTITION BY product_id ORDER BY date ROWS BETWEEN 29 PRECEDING AND CURRENT ROW). Persist the sub-query in a materialized view keyed by product_id, date for reuse in replenishment dashboards.

7. Management wants a table that flags the first day a customer qualified for Walmart+ (spend ≥ $35 twice in 30 days). Design the SQL with CTEs and window functions, and explain how you’d index the transactions table so nightly refresh stays fast.

   Use a sliding window on each customer’s chronologically ordered orders to compute 30-day spend counts, then select the first date that meets the rule. Sort keys on (customer_id, order_date) or MySQL composite indexes let the range scans stay sequential.

8. Suppose a daily sales snapshot occasionally misses late-arriving orders. Show how you’d backfill those orders and adjust running totals without double-counting.

   Strategy: load late orders into a staging table, identify affected dates/product IDs, delete those partitions in the aggregate table, and re-insert recomputed metrics. Interviewers want to see you reason about idempotent ETL and partition-level maintenance.

Advanced Walmart SQL Interview Questions (5+ Years)

Senior-level SQL interviews at Walmart—typically for senior data scientists and senior data engineers—push you into architectural thinking. These questions blur the line between SQL and systems design, often requiring you to reason about distributed computing, data quality enforcement, and schema evolution.

Expect to receive vague or open-ended prompts that require clarification and multiple assumptions. In many cases, you’ll be asked to collaborate aloud, outlining trade-offs and testing logic interactively with the interviewer.

Here’s what typically shows up:

• Building near-real-time data pipelines using SQL transformations
• Designing fault-tolerant queries that run on 10+ years of data
• Incorporating auditing logic (e.g., tracking data drift or anomalies)
• Explaining or correcting flawed logic in someone else’s query

More prompts in this section often include “explain how your query scales,” or “refactor this into a view for daily scheduled runs.”

1. Design a near-real-time pipeline that publishes total GMV by division every five minutes, while correcting for late-arriving orders that can trickle in up to 24 hours later.

   Outline the staging tables, watermark logic, and SQL merge statements you’d schedule. Discuss how your design prevents double-counting, remains idempotent, and scales when the orders fact table reaches 30 TB.

2. You inherit a 500-line production view that calculates store-level shrinkage. The query takes 45 minutes to run on a 10-year Redshift cluster. Walk through your step-by-step approach to refactor or re-index the workload so it finishes in under five minutes.

   Expect to explain dist keys, sort keys, result-set caching, and any pre-aggregation layers you would introduce.

3. Write a fault-tolerant query that backfills missing holiday-week sales for the last decade. The solution must skip days that are already correct and log audit rows for every date/product pair it rewrites.

   Interviewers want to see MERGE-style logic with change-capture checksums and an audit trail that supports downstream reconciliation.

4. Given daily inventory snapshots, calculate the 95th-percentile days-on-hand for every SKU in the past rolling year—without blowing out memory on a 4-node Snowflake XL cluster.

   Describe the percentile algorithm (e.g., APPROX_PERCENTILE or a two-stage bucketized approach) and justify partition choices that minimize data shuffling.

5. A new mobile-app rollout causes a 12 % spike in “NULL price” rows in the pos_events stream. Draft a SQL quality gate that blocks the bad data from landing in the core fact table, routes anomalies to a quarantine schema, and alerts the on-call channel.

   Be ready to discuss threshold logic, late retries, and how the gate integrates with Airflow or AWS Step Functions.

6. Create a Slowly Changing Dimension (Type 2) for store formats (Supercenter, Neighborhood Market, etc.). Show the SQL to generate surrogate keys, close previous records, and insert new versions—while maintaining queryable history across 15 years of changes.

   Write a deduplication query for a streaming click-event table where each logical event can arrive up to three times within a five-minute window. The final table must keep the earliest arrival and discard the rest, all in a single SQL step.

   Assume you have event_id, event_ts, and a SHA-256 hash of the payload.

7. Two independent ETL pipelines both calculate “weekly unique shoppers,” but their outputs differ by 4–6 %. Outline the SQL diff-check you would run to isolate the root cause, then describe the schema-level fixes you’d propose.

   Write a monitoring query that flags any Walmart Supercenter whose hourly sales deviate more than three standard deviations from its 28-day moving average, excluding Black Friday and Christmas Eve.

8. Discuss how you’d store the alert history, suppress duplicate alerts, and automate ticket creation.

   A product-recommendation view is built with eight nested CTEs that each scan the entire order_items table. Demonstrate—with SQL snippets—how you would transform it into a materialized view or incremental table, and explain the trade-offs in refresh cadence, storage, and downstream latency.

Walmart’s SQL interview process isn’t uniform across all tools and platforms—it varies depending on the stage and the role. Whether you’re facing a Walmart Karat interview, a SQLPad sandbox task, or a BigQuery-based system design, the environment and expectations will subtly shift.

Walmart Karat Round

During the Walmart Karat interview—typically a first-round technical screen for data roles—candidates are asked to live-code SQL queries while speaking their thought process aloud. These questions often center around query transformations or refactoring challenges, such as rewriting nested queries using RANK() or ROW_NUMBER(). Because this is a live setting, communication plays a big role: clear narration, especially around trade-offs or edge-case logic, can separate pass from fail.

SQLPad Sandbox

For many in-house roles, particularly at the Walmart Data Analyst or Data Engineer level, candidates complete problems using Walmart’s proprietary SQLPad interface. This environment is designed for rapid prototyping and real-time query execution. Expect tasks like choosing between a CTE and a nested subquery for better readability and performance. You may also be tested on subtle logic bugs—such as how SQLPad handles NULLs differently in aggregation clauses.

GCP BigQuery

Some Walmart teams use GCP for large-scale analytics, and this surfaces during more senior interviews. In these cases, candidates may encounter BigQuery-specific questions around partitioning, clustering, and cost optimization. For example, you might be asked to calculate rolling sales figures using window functions and justify when to use a partitioned table over a clustered one. Understanding execution costs and query scalability within BigQuery is essential, especially for system-facing data roles.

Across all platforms, make sure you’re familiar with query tuning and tool-specific idiosyncrasies—especially when preparing for Walmart phone interview questions or asynchronous take-home tasks.

Getting the SQL logic right isn’t enough—Walmart interviewers are also evaluating how well you optimize, communicate, and balance clarity with performance. These tips will help you go beyond just writing code to actually acing the technical round.

First, always clarify the metric or business goal before diving into a solution. If a question involves “conversion rate,” “return frequency,” or “fraud clusters,” ask whether there’s a baseline metric or table definition already in use. This shows you’re not just a coder—you think like a product partner.

Second, narrate your trade-offs aloud. Whether you’re choosing between a LEFT JOIN or a CASE WHEN, Walmart expects you to explain your reasoning. This is especially true in interviews that involve elements of Walmart system design interview questions, where architecture thinking is assessed.

Third, balance speed with readability. A clever-but-obscure 5-line query may get the job done, but if you can’t walk an interviewer through it—especially during a Walmart coding interview—you’ll likely miss key communication points.

Here are five essential strategies to internalize:

1. Use EXPLAIN to diagnose slowdowns.
2. Prefer CTEs over deeply nested subqueries when readability matters.
3. Always test logic on edge cases (like NULLs or 0-row inputs).
4. Know execution order—WHERE, GROUP BY, HAVING, etc.
5. Be ready to add indexing or partitioning logic based on table size hints.

For a deeper dive, explore our guides on SQL execution order, indexing strategy, and CTE best practices.

How often is SQL tested in Walmart tech interviews?

SQL is almost always tested for Walmart’s data-facing roles. Whether you’re interviewing for a Data Analyst, Data Engineer, or Scientist role, SQL is considered a core competency—expect at least one round fully dedicated to it.

What questions does Walmart ask in an interview?

Walmart interview questions span behavioral assessments, problem-solving tasks, and SQL challenges. For technical candidates, SQL and data modeling are prioritized. You’ll also see questions that test alignment with Walmart’s values.

What is the format of the Walmart SQL assessment?

Most SQL interviews occur in a live or SQLPad-based coding environment, often with questions around joins, filtering, aggregations, and window functions. Some roles involve take-home assignments with business-case prompts.

Are Walmart SQL interviews harder for senior roles?

Yes. Walmart senior data scientist interview questions often focus on large-scale data systems, complex event tracking, and optimization strategies that go beyond surface-level SQL.

What should I know about the Walmart interview process in 2025?

The Walmart interview process in 2025 follows a structured path: recruiter screen, technical screen (often via Karat), functional interviews with SQL focus, then behavioral and system design rounds.

How do I prepare for Walmart phone interview questions?

Review both behavioral and technical questions. Be prepared to code simple SQL queries and articulate business logic decisions clearly over the phone.

Are there Walmart SQL interview questions and answers PDFs available?

Yes—though they often lack context. We recommend using interactive tools or real database mocks to simulate how Walmart asks questions during live rounds.

To succeed in the Walmart SQL interview, candidates must blend strong technical fundamentals with strategic thinking and clear communication. It’s not just about knowing how to write a JOIN or calculate a rolling average—it’s about understanding the business logic behind every query and justifying your approach under pressure. Whether you’re preparing for a SQLPad exercise, a Karat live coding round, or a system design discussion, Walmart expects candidates to articulate trade-offs, consider performance implications, and reason through messy, real-world data.

Expect role-specific variations: Walmart data analyst interview questions often focus on reporting logic and metric definitions, while data engineer candidates are assessed more heavily on schema design, optimization, and pipeline fluency. As your seniority increases, so does the complexity of scenarios—ranging from multi-layered aggregations to cross-regional fraud detection.

Ultimately, interviewers are evaluating not just what you build, but how you think. So in every answer, narrate your assumptions, test your edge cases, and balance readability with scalability. For more targeted preparation, explore Interview Query’s AI Interviewer, schedule a Mock Interview, or follow our SQL Learning Path to level up from fundamentals to mastery.