eBay Coding Interview Questions

eBay’s coding interviews evaluate your ability to design fast, scalable, and transaction‑safe systems that support one of the world’s largest e‑commerce marketplaces. As an engineering candidate, you’ll be tested on algorithms, data processing, search ranking, fraud detection, and multi‑source data merging—core capabilities behind eBay’s buyer–seller ecosystem.

This guide focuses exclusively on the eBay coding interview and the types of technical challenges you’ll encounter across search, auctions, personalization, and fraud analytics.

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How eBay’s coding interview works

1. Online coding assessment

Usually includes algorithms involving:

Sorting, hashing, and two‑pointer logic
String and array manipulation
Counting and deduplication
Basic e‑commerce patterns (e.g., price tracking)

2. Technical coding interviews

Live rounds covering:

Efficient data lookups and filtering
Ranking and recommendation logic
Auction‑style price monitoring
Fraud or anomaly detection patterns

3. Debugging & optimization tasks

Some teams include exercises involving:

Fixing incorrect search or pricing logic
Improving throughput in listing pipelines
Optimizing multi‑seller data merges

Key coding concepts tested at eBay

eBay emphasizes problems involving:

Search and ranking logic

Grouping, sorting, and deduplication
Query relevance
Hot item ranking

Listing & pricing workflows

Max profit calculations
Auction price monitoring
Data validation

Fraud detection & trust signals

Outlier detection
Seller behavior monitoring
Threshold‑based alerts

Core algorithm categories

Hash maps
Priority queues
Sorting & scanning
Two‑pointer merging

eBay Coding Interview Questions (with solutions & patterns)

Below are representative eBay‑style coding challenges inspired by search, pricing, seller trust, and auction workflows.

1. Find the missing number in an array

Problem:

Return the missing value in a nearly complete sequence.

Solution outline:

Use the formula for sum of 1..n.

def find_missing_number(arr, n):

    expected_sum = n * (n + 1) // 2

    return expected_sum – sum(arr)

Concepts tested:

Arithmetic logic
Data consistency

2. Group anagrams together

Problem:

Group strings that contain the same characters.

Solution outline:

Sort characters to form a canonical key.

from collections import defaultdict

def group_anagrams(words):

    groups = defaultdict(list)

    for word in words:

        key = ”.join(sorted(word))

        groups[key].append(word)

    return list(groups.values())

Concepts tested:

Hashing
Grouping & aggregation

3. Maximum profit in eBay listings

Problem:

Find the max profit from a single buy‑sell action.

Solution outline:

Track running minimum.

def max_profit(prices):

    min_price = float(‘inf’)

    max_profit = 0

    for p in prices:

        min_price = min(min_price, p)

        max_profit = max(max_profit, p – min_price)

    return max_profit

Concepts tested:

Price deltas
Sliding tracking logic

4. Remove duplicates from sorted listings

Problem:

Deduplicate product IDs in place.

Solution outline:

Use two pointers.

def remove_duplicates(ids):

    if not ids:

        return 0

    j = 0

    for i in range(1, len(ids)):

        if ids[i] != ids[j]:

            j += 1

            ids[j] = ids[i]

    return j + 1

Concepts tested:

In‑place updates
Array scanning

5. Validate product title tags

Problem:

Ensure HTML‑like tags are balanced.

Solution outline:

Use a stack.

def validate_tags(s):

    stack = []

    for tag in s.split():

        if tag.startswith(‘<‘) and not tag.startswith(‘</’):

            stack.append(tag)

        elif tag.startswith(‘</’):

            if not stack or stack[-1][1:] != tag[2:]:

                return False

            stack.pop()

    return not stack

Concepts tested:

Stack operations
Structural validation

6. Merge k sorted seller lists

Problem:

Merge multiple sorted lists efficiently.

Solution outline:

Use a min‑heap.

import heapq

def merge_k_sorted(lists):

    heap, result = [], []

    for idx, lst in enumerate(lists):

        if lst:

            heapq.heappush(heap, (lst[0], idx, 0))

    while heap:

        val, list_idx, elem_idx = heapq.heappop(heap)

        result.append(val)

        if elem_idx + 1 < len(lists[list_idx]):

            heapq.heappush(heap, (lists[list_idx][elem_idx + 1], list_idx, elem_idx + 1))

    return result

Concepts tested:

Priority queues
Multi‑source merging

7. Detect fraudulent seller listings

Problem:

Identify suspicious listings using seller metadata + price anomalies.

Solution outline:

Compute category‑level z‑scores and combine with seller trust signals.

from collections import defaultdict

import statistics as stats

def flag_fraudulent_listings(listings, seller_stats, z_thresh=3.0):

    by_cat = defaultdict(list)

    for L in listings:

        by_cat[L[“category”]].append(L[“price”])

    cat_mu_sigma = {}

    for cat, prices in by_cat.items():

        mu = stats.mean(prices)

        sigma = stats.pstdev(prices) or 1.0

        cat_mu_sigma[cat] = (mu, sigma)

    flagged = []

    for L in listings:

        mu, sigma = cat_mu_sigma[L[“category”]]

        z = abs((L[“price”] – mu) / sigma)

        seller = seller_stats.get(L[“seller_id”], {“account_days”: 9999, “dispute_rate”: 0.0})

        risk = 0

        if seller[“account_days”] < 30:

            risk += 1

        if seller[“dispute_rate”] > 0.05:

            risk += 1

        if z >= z_thresh:

            risk += 1

        if risk >= 2:

            flagged.append({“listing_id”: L[“id”], “z”: round(z, 2), “risk”: risk})

    return flagged

Concepts tested:

Outlier detection
Risk scoring
Fraud signal aggregation

8. Price monitoring algorithm for auctions

Problem:

Detect abnormal bid changes and threshold crossings.

Solution outline:

Use sliding window and delta comparisons.

from collections import deque

def monitor_auction(bids, window=10, spike_pct=0.25, watch_targets=None):

    watch_targets = watch_targets or []

    dq = deque(maxlen=window)

    alerts, crossed = [], set()

    for ts, price in bids:

        if dq:

            prev = dq[-1][1]

            if prev > 0 and abs(price – prev) / prev >= spike_pct:

                alerts.append({“ts”: ts, “type”: “spike”, “from”: prev, “to”: price})

        for t in watch_targets:

            if t not in crossed and (dq and dq[-1][1] < t <= price):

                alerts.append({“ts”: ts, “type”: “cross_up”, “target”: t, “price”: price})

                crossed.add(t)

            elif t not in crossed and (dq and dq[-1][1] > t >= price):

                alerts.append({“ts”: ts, “type”: “cross_down”, “target”: t, “price”: price})

                crossed.add(t)

        dq.append((ts, price))

    return alerts

Concepts tested:

Auction analytics
Sliding‑window computation
Event‑trigger logic

Additional eBay-specific coding patterns

Common patterns at eBay

1. Ranking & sorting pipelines

Used in:

Search results
Trending listings

2. Fraud & anomaly detection

Appears in:

Seller trust signals
Bidding anomalies

3. Listing & inventory merging

Includes:

Multi‑source merges
Deduplication

4. Auction price analytics

Covers:

Spikes
Threshold crossing events

Difficulty levels of eBay coding questions

Easy–medium topics

Hash maps & grouping
Simple heap queries
Price deltas
Input validation

Medium–hard topics

Multi‑list merging
Fraud pattern scoring
Auction analytics
Large‑scale ranking

Role-specific variations

Backend engineering roles

Emphasize:

Search indexing logic
Listing pipelines
Ranking + personalization

Data engineering roles

Focus on:

Transaction ETL pipelines
Category‑level analytics
Price trend modeling

Machine learning roles

Expect:

Feature pipelines
Fraud detection features
Embedding‑based recommendations

Conclusion

eBay’s coding interviews highlight your ability to design scalable, reliable algorithms for search, pricing, merging, and fraud detection. Strengthen your mastery of heaps, hashing, sliding windows, and multi‑source merging to excel.

Happy learning!

Recommended resources

Grokking the Coding Interview: Strengthen your algorithmic problem-solving foundation.
Best Tips to Ace the Coding Interview: How to crack the coding interview with essential key strategies

Conclusion

Succeeding in the eBay coding interview questions means demonstrating deep technical expertise and scalable design thinking. Focus on reliability, personalization, and customer trust—pillars of eBay’s global marketplace. With consistent preparation and the right resources, you can stand out as a top candidate ready to innovate at scale.

Happy learning!

eBay Coding Interview Questions

How eBay’s coding interview works

1. Online coding assessment

2. Technical coding interviews

3. Debugging & optimization tasks

Key coding concepts tested at eBay

Search and ranking logic

Listing & pricing workflows

Fraud detection & trust signals

Core algorithm categories

eBay Coding Interview Questions (with solutions & patterns)

1. Find the missing number in an array

2. Group anagrams together

3. Maximum profit in eBay listings

4. Remove duplicates from sorted listings

5. Validate product title tags

6. Merge k sorted seller lists

7. Detect fraudulent seller listings

8. Price monitoring algorithm for auctions

Additional eBay-specific coding patterns

Common patterns at eBay

1. Ranking & sorting pipelines

2. Fraud & anomaly detection

3. Listing & inventory merging

4. Auction price analytics

Difficulty levels of eBay coding questions

Easy–medium topics

Medium–hard topics

Role-specific variations

Backend engineering roles

Data engineering roles

Machine learning roles

Conclusion

Recommended resources

Conclusion

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