Walmart Data Analyst Interview Guide

From hundreds of mock interviews we've run, the candidates who get filtered out fastest from Walmart aren't the ones with weak SQL. They're the ones who over-index on algorithms and show up unable to explain why comp-store sales moved last quarter. Walmart's interview process rewards business acumen and stakeholder storytelling over technical complexity, and most people prep in exactly the wrong ratio.

Walmart Data Analyst Role

Your job is to sit between Walmart's massive distributed data systems (Hive, Spark, BigQuery, Tableau, Looker, Power BI, depending on your team) and the merchant directors, supply chain leads, and fraud/risk partners who need answers fast enough to act on them. Success after year one means you own a reporting framework stakeholders actually trust, you've diagnosed metric anomalies without hand-holding, and your dashboards are the ones pulled up in weekly leadership reviews.

A Typical Week

The split that surprises most candidates is how little time goes to pure coding (around 15%) versus how much goes to analysis and writing (45% combined). You're not heads-down in notebooks all day. The real work is translating complex Hive or BigQuery output into a concise deck that lands a recommendation with a director who's asking pointed margin questions in real time. Fridays look quiet on paper, but peer-reviewing LookML join logic or QA-ing an executive scorecard is the kind of unglamorous work that prevents a silent data error from distorting a massive category's revenue report.

Projects & Impact Areas

Demand forecasting support is where your data quality work has the most direct physical-world impact, since cleaned signals you feed into Walmart's replenishment tech stack determine whether shelves are stocked or bare across tens of thousands of SKUs. That work increasingly intersects with omnichannel analytics as Walmart bridges in-store and e-commerce customer behavior through Pickup & Delivery expansion. Fraud detection and risk analytics represent a distinct, high-priority track: you'll identify trends and patterns in transaction data, perform root cause analysis on anomalies, and support risk management decisions that protect revenue at enormous scale.

Skills & What's Expected

Business acumen and data storytelling are both rated at expert level for this role, and that's not aspirational language. SQL and data architecture matter too (think star schemas and slowly changing dimensions, not just SELECT statements), but machine learning and software engineering expectations are low. The implication: overrating your ML chops while underrating your ability to tell a concise, dollar-denominated story about fraud trends or promo effectiveness is the fastest path to a "no." Focus your prep on hypothesis testing, trend decomposition, and the ability to determine whether a promotion actually moved the needle or just cannibalized an adjacent category.

Levels & Career Growth

Data Analyst II is the most common mid-level entry point. What separates a II from a Senior isn't query complexity; it's cross-functional influence. Do merchandising partners come to you proactively, or do you wait for tickets? Growth paths fork toward Senior Data Scientist (more modeling), Analytics Manager (people leadership), or specialized tracks like risk analytics. Lateral moves across Walmart U.S., Sam's Club, and Walmart International are common given shared data infrastructure, so the promotion blocker is rarely "no open roles." It's demonstrating you shaped decisions, not just delivered dashboards.

Work Culture

Walmart's expectation is primarily on-site at Bentonville or hub offices, with some teams allowing one remote day per week. Factor that into your decision seriously if you're currently remote-first. The culture is frugal and execution-oriented: presentations should be concise, data-backed, and tied to a dollar impact, not exploratory research decks without a clear "so what." Most analysts work roughly 8:30 to 5:30, with occasional longer days around key retail events, but weekend work is rare outside peak seasons.

Walmart Data Analyst Compensation

Walmart RSUs vest over several years, with the source data suggesting a standard annual schedule. There's no cliff to worry about, but equity grants have less flexibility in negotiation than base salary or sign-on bonuses, so don't burn negotiation capital trying to push RSU numbers up. Walmart stock behaves like a slow compounder, not a moonshot, which matters when you're mentally valuing your total package.

Sign-on bonuses and base salary are where you have real room to negotiate, especially if you can present a competing offer from another retailer or analytics-heavy company. Walmart's retail operations run on razor-thin margins and a frugal culture, so frame your ask around the value you bring to their specific data challenges (shrink analysis, demand forecasting, omnichannel measurement) rather than abstract market benchmarks.

Walmart Data Analyst Interview Process

Weak SQL is one of the most frequent blockers, according to candidate reports. But it's not just about getting the right answer. The case study and behavioral rounds filter out nearly as many people for unstructured problem-solving and failing to connect analysis back to business impact, two rejection patterns the data consistently shows.

Here's what most candidates miss about the final round: the hiring manager conversation carries real weight in the decision. That interviewer is evaluating whether your project examples show concrete recommendations and measurable outcomes, not just interesting explorations. If your best stories end with "I built a dashboard" instead of "the merchandising team changed their replenishment cadence and reduced overstock by X%," you're vulnerable even after strong technical rounds.

Walmart Data Analyst Interview Questions

/*
Assumptions (BigQuery SQL):
- order_items(order_id, order_item_id, product_id, store_id, purchase_ts, quantity, item_price, item_discount)
- dim_product_scd(product_id, category, effective_start_ts, effective_end_ts, updated_at)
- Net sales = quantity * (item_price - item_discount)
- "Last week" = previous complete week in UTC (Mon-Sun). Adjust date logic if your business week differs.
*/

WITH params AS (
  SELECT
    -- Previous complete week boundaries
    TIMESTAMP(DATE_TRUNC(DATE_SUB(CURRENT_DATE('UTC'), INTERVAL 1 WEEK), WEEK(MONDAY)), 'UTC') AS week_start_ts,
    TIMESTAMP(DATE_ADD(DATE_TRUNC(DATE_SUB(CURRENT_DATE('UTC'), INTERVAL 1 WEEK), WEEK(MONDAY)), INTERVAL 7 DAY), 'UTC') AS week_end_ts
),
filtered_items AS (
  SELECT oi.*
  FROM order_items oi
  CROSS JOIN params p
  WHERE oi.purchase_ts >= p.week_start_ts
    AND oi.purchase_ts < p.week_end_ts
),
scd_match AS (
  SELECT
    oi.order_id,
    oi.order_item_id,
    oi.product_id,
    oi.purchase_ts,
    oi.quantity,
    oi.item_price,
    oi.item_discount,
    dp.category,
    ROW_NUMBER() OVER (
      PARTITION BY oi.order_id, oi.order_item_id
      ORDER BY dp.effective_start_ts DESC, dp.updated_at DESC
    ) AS rn
  FROM filtered_items oi
  JOIN dim_product_scd dp
    ON dp.product_id = oi.product_id
   AND oi.purchase_ts >= dp.effective_start_ts
   AND oi.purchase_ts < COALESCE(dp.effective_end_ts, TIMESTAMP '9999-12-31 00:00:00+00')
)
SELECT
  category,
  SUM(quantity * (item_price - item_discount)) AS net_sales
FROM scd_match
WHERE rn = 1
GROUP BY category
ORDER BY net_sales DESC;

/*
Assumptions (BigQuery SQL):
- order_items(order_id, order_item_id, sku_id, store_id, purchase_ts, quantity, item_price, item_discount)
- Net sales = quantity * (item_price - item_discount)
- Most recent complete week is the previous Monday-Sunday week in UTC.
*/

WITH params AS (
  SELECT
    DATE_TRUNC(DATE_SUB(CURRENT_DATE('UTC'), INTERVAL 1 WEEK), WEEK(MONDAY)) AS curr_week_start,
    DATE_TRUNC(DATE_SUB(CURRENT_DATE('UTC'), INTERVAL 2 WEEK), WEEK(MONDAY)) AS prev_week_start
),
weekly_sku_sales AS (
  SELECT
    oi.store_id,
    oi.sku_id,
    DATE_TRUNC(DATE(oi.purchase_ts, 'UTC'), WEEK(MONDAY)) AS week_start,
    SUM(oi.quantity * (oi.item_price - oi.item_discount)) AS net_sales
  FROM order_items oi
  CROSS JOIN params p
  WHERE DATE(oi.purchase_ts, 'UTC') >= p.prev_week_start
    AND DATE(oi.purchase_ts, 'UTC') < DATE_ADD(p.curr_week_start, INTERVAL 7 DAY)
  GROUP BY 1, 2, 3
),
pair_weeks AS (
  SELECT
    curr.store_id,
    curr.sku_id,
    curr.week_start AS curr_week_start,
    curr.net_sales AS curr_net_sales,
    prev.net_sales AS prev_net_sales,
    SAFE_DIVIDE(curr.net_sales - prev.net_sales, prev.net_sales) AS wow_growth
  FROM weekly_sku_sales curr
  JOIN weekly_sku_sales prev
    ON prev.store_id = curr.store_id
   AND prev.sku_id = curr.sku_id
   AND prev.week_start = DATE_SUB(curr.week_start, INTERVAL 7 DAY)
  CROSS JOIN params p
  WHERE curr.week_start = p.curr_week_start
    AND curr.net_sales >= 1000
    AND prev.net_sales >= 1000
),
ranked AS (
  SELECT
    store_id,
    sku_id,
    curr_week_start,
    curr_net_sales,
    prev_net_sales,
    wow_growth,
    ROW_NUMBER() OVER (
      PARTITION BY store_id
      ORDER BY wow_growth DESC, curr_net_sales DESC, sku_id ASC
    ) AS rn
  FROM pair_weeks
)
SELECT
  store_id,
  sku_id,
  curr_week_start,
  prev_net_sales,
  curr_net_sales,
  wow_growth
FROM ranked
WHERE rn <= 3
ORDER BY store_id, rn;

MERGE `prod.funnel_daily` T
USING (
  WITH base AS (
    SELECT
      DATE(event_ts) AS event_date,
      session_id,
      user_id,
      event_name,
      event_ts,
      event_id,
      ROW_NUMBER() OVER (PARTITION BY event_id ORDER BY ingest_ts DESC) AS rn
    FROM `raw.web_events`
    WHERE DATE(event_ts) BETWEEN DATE_SUB(@run_date, INTERVAL 3 DAY) AND @run_date
  )
  SELECT
    event_date,
    session_id,
    user_id,
    event_name,
    event_ts,
    event_id
  FROM base
  WHERE rn = 1
) S
ON T.event_id = S.event_id
WHEN MATCHED THEN
  UPDATE SET
    event_date = S.event_date,
    session_id = S.session_id,
    user_id = S.user_id,
    event_name = S.event_name,
    event_ts = S.event_ts
WHEN NOT MATCHED THEN
  INSERT (event_date, session_id, user_id, event_name, event_ts, event_id)
  VALUES (S.event_date, S.session_id, S.user_id, S.event_name, S.event_ts, S.event_id);

SELECT
  store_id,
  item_id,
  SUM(net_sales) AS net_sales
FROM store_sales
WHERE ds BETWEEN '${start_ds}' AND '${end_ds}'
  AND store_id = ${store_id}
  AND item_id = ${item_id}
GROUP BY store_id, item_id;

What catches people off guard is how the case study and SQL rounds bleed into each other. A question might start as a diagnosis problem (why did produce margins drop 200bps in the Southeast?) and then pivot to "write the Spark SQL that would validate your top hypothesis against the transactions fact table." Prepping these skills in isolation, treating cases as slide-deck exercises and SQL as syntax drills, leaves you unprepared for the moment they collide. Meanwhile, data modeling and pipeline questions together probe whether you can design schemas and partition strategies that keep Walmart's Tableau dashboards from silently miscounting revenue across 10,500+ stores, the kind of infrastructure thinking most candidates skip entirely in favor of brushing up on statistical tests they'll barely be asked about.

Sharpen your retail case study and SQL skills with practice problems at datainterview.com/questions.

How to Prepare for Walmart Data Analyst Interviews

Walmart is placing two big bets that define what analysts actually spend their time on. The Google AI shopping partnership launched in early 2026 turns natural-language product discovery into checkout, creating new attribution puzzles across store, pickup, and delivery channels. Simultaneously, Walmart rolled back prices on 1,000+ wellness essentials and launched new care services, generating cross-category data that analysts need to stitch together for merchandising leads who want a one-page answer, not a data dump.

Both initiatives sit on top of a demand forecasting tech stack that relies on analyst-curated signals to replenish 100K+ SKUs. With FY2025 revenue hitting $681 billion for Walmart U.S. alone and 5.8% year-over-year growth company-wide, a single broken metric definition in that pipeline can misallocate millions in inventory spend.

Your "why Walmart" answer should name one of these active bets and the analyst-level problem it creates. Talk about how the omnichannel push makes it hard to attribute a Walmart+ member's grocery basket when they browse on Google, add to cart on the app, then switch to in-store pickup. Or reference how the wellness price rollbacks require analysts to separate real demand lift from cannibalization of adjacent health categories. That kind of specificity maps to the actual job in a way that "I love retail data at scale" never will.

Try a Real Interview Question

| sales_daily |            |             |
|------------|------------|-------------|
| store_id   | sales_date | sales_dollars |
|------------|------------|-------------|
| 101        | 2025-01-01 | 10000       |
| 101        | 2025-01-02 | 12000       |
| 101        | 2025-01-08 | 9000        |
| 102        | 2025-01-01 | 8000        |
| 102        | 2025-01-08 | 7000        |

| fraud_events |            |              |            |
|-------------|------------|--------------|------------|
| store_id    | event_date | fraud_dollars | channel    |
|-------------|------------|--------------|------------|
| 101         | 2025-01-02 | 50           | POS        |
| 101         | 2025-01-08 | 400          | ECOM       |
| 102         | 2025-01-01 | 20           | POS        |
| 102         | 2025-01-08 | 30           | POS        |
| 101         | 2025-01-01 | 10           | POS        |

WITH sales_wk AS (
  SELECT
    store_id,
    DATE_TRUNC(sales_date, WEEK(MONDAY)) AS week_start,
    SUM(CAST(sales_dollars AS NUMERIC)) AS sales_dollars
  FROM sales_daily
  GROUP BY 1, 2
),
fraud_wk AS (
  SELECT
    store_id,
    DATE_TRUNC(event_date, WEEK(MONDAY)) AS week_start,
    SUM(CAST(fraud_dollars AS NUMERIC)) AS fraud_dollars
  FROM fraud_events
  GROUP BY 1, 2
),
weekly AS (
  SELECT
    s.store_id,
    s.week_start,
    s.sales_dollars,
    COALESCE(f.fraud_dollars, 0) AS fraud_dollars,
    SAFE_DIVIDE(COALESCE(f.fraud_dollars, 0), NULLIF(s.sales_dollars, 0)) AS shrink_rate
  FROM sales_wk s
  LEFT JOIN fraud_wk f
    ON s.store_id = f.store_id
   AND s.week_start = f.week_start
),
stats AS (
  SELECT
    store_id,
    AVG(shrink_rate) AS mu,
    STDDEV_SAMP(shrink_rate) AS sigma
  FROM weekly
  GROUP BY 1
)
SELECT
  w.store_id,
  w.week_start,
  w.sales_dollars,
  w.fraud_dollars,
  w.shrink_rate,
  CASE
    WHEN w.shrink_rate > (s.mu + 2 * COALESCE(s.sigma, 0)) THEN 1
    ELSE 0
  END AS is_anomaly
FROM weekly w
JOIN stats s
  ON w.store_id = s.store_id
ORDER BY w.store_id, w.week_start;

Retail schemas with transactions, stores, and products joined across time periods are the bread and butter here. From what candidates report, the emphasis is less on trick syntax and more on whether you can structure a query that would still run reasonably on very large tables. Sharpen that skill with retail-shaped SQL problems at datainterview.com/coding.

Test Your Readiness

Pay special attention to case questions involving comp-store sales diagnosis and promo cannibalization, two scenarios that come up repeatedly in Walmart loops. Practice more at datainterview.com/questions.