11 Walmart Global Tech Software Engineer (New Grad) Interview Questions (2026)

Outline

STAR. Walmart's culture explicitly cites 'Customer Focus' and 'Act with Integrity' and emphasizes ownership. Pick a moment where you owned not just the task but the outcome — debugging through layers, escalating cleanly, or sticking with a hard problem.

Outline

Specifics: scale (largest US retailer by revenue), the engineering reinvention under Walmart Global Tech, the breadth (e-commerce, supply chain, in-store tech, advertising), or the move toward distributed leadership. Avoid 'I want a stable big company' framings.

Outline

STAR. Walmart values resilience. Pick a real moment with concrete obstacles (deadline slipped, dependency broken, scope grew) and how you navigated. Show you can sustain effort, not just dramatize struggle.

Outline

STAR. Walmart's leadership principles include 'Adapt to Win' and 'Innovate to Lead.' Pick a moment when you cut work, automated something repetitive, or replaced a tangled process. Be specific on time saved or errors avoided.

Outline

Min-heap of size K. Iterate, push, pop if size > K. End of pass, heap holds top K. O(N log K) time, O(K) space. Beats sorting (O(N log N)) when K is small. Be ready for the variant with ties (return all ties or just K — clarify).

Outline

In-order traversal yields nodes in sorted order. Recursive or iterative (stack). Stop at k-th visit. O(H + k) time. Mention augmented-BST variant (each node stores subtree size) for O(log N) queries when there are many queries.

Outline

Backtracking. Each segment is 1-3 digits, value 0-255, no leading zeros (except '0' itself). Try lengths 1, 2, 3 at each position, recurse, append valid IPs at depth 4. Reject invalid early to prune. O(1) worst case (bounded by 4 segments × max splits).

Outline

SQL: structured schema, ACID transactions, joins, vertical scaling typically. NoSQL document store: schema-flexible JSON-shaped documents, often eventually-consistent, horizontal scaling, embedded relationships. Use SQL for order data (ACID needed), NoSQL for catalog/session/cart (high read throughput, flexible schema).

Outline

Connected components via union-find or DFS. For weighted edges, use a community-detection algorithm (Louvain, label propagation). Discuss what 'cluster' means in this domain — co-purchase patterns vs categorical similarity. Walmart Global Tech runs real ML for recommendation; this question probes ML-adjacent reasoning.

Outline

Cart service → order service → payment service → inventory reservation → fulfillment. Discuss idempotency (a double-click cannot create two orders), inventory race conditions (oversell vs lost sale), payment retries, transactional outbox pattern for downstream events. Scale: millions of orders per day means hot SKUs need extra care.

Outline

Inventory service backed by a distributed store. Eventually-consistent view sufficient for most reads (catalog browsing) but the buy-action must check against a strongly-consistent reservation. Cache reads aggressively. Update path: each store and warehouse emits inventory deltas, an aggregator computes the view. Discuss reconciliation jobs for drift.