Walmart Data Engineer Interview Guide

Walmart's data engineering org sits behind one of the largest retail data platforms outside FAANG, feeding pipelines that drive store-level replenishment, omnichannel fulfillment, and pricing decisions across every Walmart and Sam's Club location in the country. What surprises most candidates prepping for this loop is how heavily it skews toward pipeline architecture and system design, not ML or GenAI.

Walmart Data Engineer Role

Your job is to build and maintain the Spark, Kafka, and Airflow pipelines that feed Walmart's demand forecasting systems, stitch together in-store POS transactions with walmart.com clickstream and Sam's Club scan-and-go events, and keep petabyte-scale Delta/Iceberg tables fresh enough for downstream teams to trust. After year one, success looks like owning a specific pipeline domain (the nightly inventory replenishment feed, the real-time pricing event stream) and having migrated at least one legacy Hadoop batch job onto Spark-on-Databricks.

A Typical Week

Infrastructure work and coding each claim 25% of your week, which means you'll spend as much time resizing Kafka consumer partitions and tracing upstream Oracle schema changes as you will writing PySpark transformations. Meetings (20%) are mostly standups, design reviews, and cross-functional syncs with supply chain and data science teams who consume your pipelines. On-call weeks shift the balance hard toward triage, so don't plan deep feature work during those rotations.

Projects & Impact Areas

Walmart's demand forecasting pipeline is the headline project: a Spark-and-Kafka stack powering store-level replenishment decisions that determine whether shelves are actually stocked. Omnichannel data integration runs alongside it, joining Sam's Club membership events with marketplace seller feeds and grocery pickup orders into unified datasets that feed Walmart's Google-partnered product discovery and personalization models. Data quality at this scale is its own workstream, with Great Expectations checks, lineage tracking, and schema evolution enforcement across hundreds of upstream sources.

Skills & What's Expected

Data architecture and pipeline design is rated "expert" here, meaning even DE II candidates need to reason about idempotent ETL, schema evolution in Avro/Parquet, and exactly-once delivery in streaming contexts. Software engineering (clean Python/PySpark, CI/CD via GitHub Actions, Terraform) is "high" and non-negotiable. ML and GenAI score "low" as core requirements, though GenAI is an active team initiative and shows up as a preferred skill, so don't ignore it entirely. The underrated dimension? Business acumen around retail supply chain. Interviewers want you to explain why a null rate spike in a warehouse_location column matters for store fulfillment, not just how to fix it.

Levels & Career Growth

The jump from Senior to Staff is where careers stall, because Staff requires cross-org platform influence, not just reliable ownership of your team's pipelines. Walmart's InnerSource culture (internal open-source contributions visible across the company) gives ICs a concrete path to build that reputation without switching to management. Principal roles demand architecture ownership spanning multiple business units and are, from what candidates report, quite rare.

Work Culture

Most data engineering roles follow a hybrid model requiring three days per week in the Bentonville office, though some Walmart Global Tech teams have more flexibility for remote work. The daily rhythm runs roughly 8:30 to 5:30 and is genuinely respected outside on-call weeks. Sam Walton's frugality DNA persists in ways you'll feel: you're expected to optimize Spark job costs before requesting more cluster capacity, and infrastructure vendor negotiations reflect a scrappiness that's refreshing if you're coming from a company that throws compute at every problem.

Walmart Data Engineer Compensation

Walmart RSUs follow a standard annual vesting schedule, from what candidates report, roughly 25% per year over four years. The base salary and sign-on bonus are the most negotiable components of a Walmart offer, while RSU grant totals tend to have less flexibility. If you're weighing an offer, push hardest on those two levers during the recruiter screen rather than waiting for the final round.

Walmart's offer negotiation notes explicitly call out competing offers as useful leverage. Bring a credible alternative, even from a non-tech retailer or a mid-tier company, and be specific about the delta. The more concrete your competing number, the easier it is for your recruiter to justify a bump internally. Don't forget to factor in Walmart's associate stock purchase plan and benefits package when comparing total value across offers.

Walmart Data Engineer Interview Process

The loop runs about five weeks end to end across five rounds. DSA is explicitly the first technical filter, and the source data backs that up, but weak SQL and system design performances are equally fatal since those rounds probe the exact skills Walmart's petabyte-scale retail pipelines demand daily. Candidates who prep only for coding and neglect schema design for retail scenarios (think slowly changing dimensions on SKU-level sales data) tend to wash out mid-loop.

Your hiring manager screen feels conversational, but don't mistake tone for low stakes. From what candidates report, this round carries real weight because it tests whether you can talk about data quality incidents, collaborate with non-technical supply chain partners, and demonstrate the kind of cost-conscious scrappiness Walmart's culture rewards. A strong showing here won't save a bombed technical round, yet a flat one can sink an otherwise solid loop.

Walmart Data Engineer Interview Questions

from pyspark.sql import functions as F

# Assumed schema: order_id, event_type, event_ts (timestamp), amount, currency, version, ingestion_ts
# Goal: build a deduped Silver table keyed by (order_id, event_type) or (order_id, version)

raw = (
    spark.readStream.format("kafka")
    .option("kafka.bootstrap.servers", "${KAFKA_BROKERS}")
    .option("subscribe", "checkout_order_events")
    .load()
)

# Parse payload (placeholder, adjust to your serializer)
parsed = (
    raw.selectExpr("CAST(value AS STRING) AS json_str")
    .select(F.from_json("json_str", "order_id STRING, event_type STRING, event_ts TIMESTAMP, amount DOUBLE, version LONG, ingestion_ts TIMESTAMP").alias("r"))
    .select("r.*")
)

# Watermark on event time, then drop duplicates by stable key
# If you have an immutable event_id, use that. Otherwise use (order_id, event_type, version).
deduped = (
    parsed
    .withWatermark("event_ts", "48 hours")
    .dropDuplicates(["order_id", "event_type", "version"])  # adjust for your contract
)

# Write deduped events to Silver as a Delta table (or equivalent lakehouse table)
# Use foreachBatch so each micro-batch can MERGE (idempotent upsert)

def upsert_to_silver(microBatchDf, batchId):
    microBatchDf.createOrReplaceTempView("updates")

    spark.sql(
        """
        MERGE INTO silver_checkout_order_events t
        USING updates s
        ON t.order_id = s.order_id
           AND t.event_type = s.event_type
           AND t.version = s.version
        WHEN MATCHED THEN UPDATE SET *
        WHEN NOT MATCHED THEN INSERT *
        """
    )

(
    deduped.writeStream
    .outputMode("update")
    .foreachBatch(upsert_to_silver)
    .option("checkpointLocation", "${CHECKPOINT_PATH}/silver_checkout_order_events")
    .start()
)

# Downstream hourly net sales should be computed from the keyed Silver table, not directly from raw stream.

WITH ranked AS (
  SELECT
    store_id,
    sku_id,
    business_date,
    on_hand_units,
    ingest_ts,
    load_id,
    ROW_NUMBER() OVER (
      PARTITION BY store_id, sku_id, business_date
      ORDER BY ingest_ts DESC, load_id DESC
    ) AS rn
  FROM inventory_snapshot
),
latest AS (
  SELECT
    store_id,
    sku_id,
    business_date,
    on_hand_units
  FROM ranked
  WHERE rn = 1
)
SELECT
  store_id,
  sku_id,
  business_date,
  on_hand_units,
  on_hand_units
    - LAG(on_hand_units) OVER (
        PARTITION BY store_id, sku_id
        ORDER BY business_date
      ) AS on_hand_delta_vs_yesterday
FROM latest
ORDER BY store_id, sku_id, business_date;

WITH params AS (
  SELECT
    CURRENT_DATE AS as_of_date,
    CURRENT_DATE - INTERVAL '30' DAY AS start_date
),
orders_30d AS (
  SELECT
    ol.order_id,
    ol.store_id,
    ol.sku_id,
    CAST(ol.order_ts AS DATE) AS business_date,
    ol.qty,
    ol.unit_price
  FROM order_line ol
  JOIN params p
    ON CAST(ol.order_ts AS DATE) >= p.start_date
   AND CAST(ol.order_ts AS DATE) < p.as_of_date
),
returns_agg AS (
  -- Aggregate to prevent join fanout when multiple return events exist per order line.
  SELECT
    r.order_id,
    r.sku_id,
    CAST(r.return_ts AS DATE) AS business_date,
    SUM(r.return_qty) AS return_qty
  FROM returns r
  JOIN params p
    ON CAST(r.return_ts AS DATE) >= p.start_date
   AND CAST(r.return_ts AS DATE) < p.as_of_date
  GROUP BY 1, 2, 3
),
return_revenue AS (
  -- Price returns using the original order unit_price.
  SELECT
    o.store_id,
    o.sku_id,
    ra.business_date,
    SUM(ra.return_qty * o.unit_price) AS return_revenue
  FROM returns_agg ra
  JOIN orders_30d o
    ON o.order_id = ra.order_id
   AND o.sku_id = ra.sku_id
  GROUP BY 1, 2, 3
),
sku_dim_current AS (
  -- Assume a Type 1 or current snapshot dimension for the analytics view.
  SELECT
    sku_id,
    department_id
  FROM dim_sku_current
),
gross_by_day AS (
  SELECT
    o.store_id,
    d.department_id,
    o.business_date,
    SUM(o.qty * o.unit_price) AS gross_sales
  FROM orders_30d o
  JOIN sku_dim_current d
    ON d.sku_id = o.sku_id
  GROUP BY 1, 2, 3
),
returns_by_day AS (
  SELECT
    rr.store_id,
    d.department_id,
    rr.business_date,
    SUM(rr.return_revenue) AS returns_sales
  FROM return_revenue rr
  JOIN sku_dim_current d
    ON d.sku_id = rr.sku_id
  GROUP BY 1, 2, 3
)
SELECT
  COALESCE(g.store_id, r.store_id) AS store_id,
  COALESCE(g.department_id, r.department_id) AS department_id,
  COALESCE(g.business_date, r.business_date) AS business_date,
  COALESCE(g.gross_sales, 0.0) - COALESCE(r.returns_sales, 0.0) AS net_sales
FROM gross_by_day g
FULL OUTER JOIN returns_by_day r
  ON r.store_id = g.store_id
 AND r.department_id = g.department_id
 AND r.business_date = g.business_date
ORDER BY store_id, department_id, business_date;

from __future__ import annotations

from dataclasses import dataclass
from datetime import datetime
from typing import Iterable, List, Tuple, Dict, Optional


Event = Tuple[str, str, datetime, str]  # (store_id, item_id, event_ts, event_type)


def latest_events_per_item(events: Iterable[Event]) -> List[Event]:
    """Return the latest event per (store_id, item_id).

    Tie-break rule for same (store_id, item_id, event_ts): prefer event_type == 'SALE'.
    If both are SALE or both non-SALE, keep the first seen (stable).

    Time complexity: O(n)
    Space complexity: O(k) where k is number of unique (store_id, item_id)
    """

    def better(a: Event, b: Event) -> bool:
        """True if event a should replace event b."""
        _, _, ts_a, type_a = a
        _, _, ts_b, type_b = b

        if ts_a > ts_b:
            return True
        if ts_a < ts_b:
            return False

        # Same timestamp: SALE wins over non-SALE.
        a_sale = type_a == "SALE"
        b_sale = type_b == "SALE"
        if a_sale and not b_sale:
            return True
        if not a_sale and b_sale:
            return False

        # Same priority, keep existing (stable).
        return False

    best: Dict[Tuple[str, str], Event] = {}
    for e in events:
        store_id, item_id, _, _ = e
        key = (store_id, item_id)
        if key not in best or better(e, best[key]):
            best[key] = e

    return list(best.values())


# Minimal self-checks
if __name__ == "__main__":
    t1 = datetime.fromisoformat("2024-01-01T10:00:00")
    t2 = datetime.fromisoformat("2024-01-01T10:05:00")

    inp: List[Event] = [
        ("101", "SKU1", t1, "VIEW"),
        ("101", "SKU1", t1, "SALE"),  # tie on ts, SALE wins
        ("101", "SKU2", t2, "RETURN"),
        ("101", "SKU2", t1, "SALE"),  # older, should lose
        ("102", "SKU1", t2, "VIEW"),
    ]

    out = latest_events_per_item(inp)
    m = {(s, i): (ts, et) for (s, i, ts, et) in out}
    assert m[("101", "SKU1")] == (t1, "SALE")
    assert m[("101", "SKU2")] == (t2, "RETURN")
    assert m[("102", "SKU1")] == (t2, "VIEW")

The distribution skews hard toward questions where you're reasoning about Walmart's actual infrastructure: ingesting POS transactions across 10,500+ stores, handling 48-hour late arrivals on Walmart.com order streams, reconciling inventory across fulfillment nodes. Pipeline and system design questions compound on each other, because a candidate who can't explain idempotent Bronze-to-Silver loading in the pipeline round won't suddenly produce a credible near-real-time inventory architecture in the design round. Candidates coming from other big tech prep cycles tend to burn most of their hours on coding and SQL drills, then get caught flat-footed when asked to model SCD handling for Walmart's Item dimension or design exactly-once delivery for store receipt streams.

Drill Walmart-specific pipeline, modeling, and SQL scenarios at datainterview.com/questions.

How to Prepare for Walmart Data Engineer Interviews

The widget covers Walmart's strategy and financials, so let's talk about what that means for your prep. Read the demand forecasting tech stack post on Walmart Global Tech's Medium blog before your system design round. It walks through how Spark, Kafka, and custom lakehouse layers feed store replenishment decisions, and interviewers from that org have been known to probe whether you understand the tradeoffs they made (batch vs. micro-batch, exactly-once semantics at retail scale). The Google partnership for AI-driven product discovery is also worth studying because it reveals where new pipeline work is heading: feeding personalization models that blend in-store and online signals.

Your "why Walmart" answer should name a specific data problem you'd want to solve, not praise the company's size. Saying "I want to work on stitching POS transactions with walmart.com clickstream under sub-minute freshness SLAs" shows you understand the actual engineering tension. Walmart's InnerSource culture also gives you a credible angle: you can talk about wanting to contribute to shared platform tooling across teams, which resonates more than generic "scale" enthusiasm.

Try a Real Interview Question

| shipment_id | dc_id | store_id | planned_depart_ts    | actual_depart_ts     | status    |
|-------------|-------|----------|----------------------|----------------------|-----------|
| S1          | DC1   | 101      | 2026-02-01 08:00:00  | 2026-02-01 08:10:00  | DEPARTED  |
| S2          | DC1   | 102      | 2026-02-01 09:00:00  | 2026-02-01 08:55:00  | DEPARTED  |
| S3          | DC1   | 103      | 2026-02-02 07:30:00  | 2026-02-02 08:05:00  | DEPARTED  |
| S4          | DC2   | 201      | 2026-02-01 10:00:00  | 2026-02-01 10:00:00  | DEPARTED  |
| S5          | DC2   | 202      | 2026-02-01 11:00:00  | 2026-02-01 11:20:00  | DEPARTED  |

WITH base AS (
  SELECT
    dc_id,
    CAST(planned_depart_ts AS DATE) AS ship_date,
    shipment_id,
    CASE
      WHEN actual_depart_ts > planned_depart_ts THEN 1
      ELSE 0
    END AS is_late
  FROM shipments
  WHERE status = 'DEPARTED'
    AND planned_depart_ts IS NOT NULL
    AND actual_depart_ts IS NOT NULL
)
SELECT
  dc_id,
  ship_date,
  COUNT(*) AS total_shipments,
  SUM(is_late) AS late_shipments,
  ROUND(1.0 * SUM(is_late) / NULLIF(COUNT(*), 0), 3) AS late_rate
FROM base
GROUP BY dc_id, ship_date
HAVING COUNT(*) >= 2
ORDER BY ship_date, dc_id;

Walmart's coding round, from what candidates report on interview experience posts, skews toward parsing nested retail data formats and reconciling mismatched schemas rather than textbook dynamic programming. You'll want reps on file transformations, DAG traversal, and schema validation problems. Build that muscle at datainterview.com/coding.

Test Your Readiness

Walmart's SQL round in particular catches people off guard with window functions over time-series sales data and billion-row optimization constraints. Drill Walmart-tagged questions at datainterview.com/questions to spot your gaps early.