eBay Software Engineer Interview Guide: Questions, Process & Salary

The eBay software engineer interview questions are crafted to evaluate not just your technical skillset, but your ability to build scalable, user-facing features in one of the world’s largest online marketplaces. Whether you’re working on search infrastructure, payments, or buyer experience, the eBay software engineer interview process is structured to assess how you approach real-world engineering challenges.

At eBay, software engineers work at the intersection of commerce and technology—building tools that power over a billion listings and serve millions of buyers and sellers daily. From back-end systems to front-end optimization, you’ll be expected to write clean, efficient code, collaborate in agile pods, and contribute to a culture that values speed, experimentation, and inclusive opportunity. If you’re preparing, be ready for a range of challenges including data structures, system design, and practical eBay coding interview questions that mirror production environments.

Role Overview & Culture

As a software engineer at eBay, you’ll own features end-to-end—writing, testing, and shipping code that operates at global marketplace scale. You’ll work in agile product pods alongside PMs, designers, and other engineers to quickly iterate on user needs. From seller tools to trust & safety systems, engineers are encouraged to experiment, deploy frequently, and drive real-world impact.

The culture is rooted in using technology to enable economic opportunity—whether it’s helping a solo seller reach global buyers or improving personalization for millions. eBay’s engineering org values autonomy, clean architecture, and measurable outcomes over flashy code.

Why This Role at eBay?

This role offers the chance to make an immediate difference—touching high-traffic systems that shape how people buy, sell, and connect online. Engineers at eBay benefit from clear career paths (Software Engineer → Senior → Staff → Principal), competitive compensation, and the support of a company that invests in both open-source contributions and internal learning.

To land the role, you’ll face a multi-stage eBay interview process designed to test your coding fundamentals, system design thinking, and cultural alignment. Let’s walk through what that journey looks like—and how to prepare for each step.

The eBay software engineer interview process is structured to test for technical depth, product intuition, and cultural fit. Whether you’re applying for a new grad or a senior role, you’ll go through multiple stages designed to evaluate both your coding fluency and your ability to build scalable, user-centric systems.

Application & Recruiter Screen

This initial step focuses on resume alignment, role fit, and your motivation for joining eBay. Recruiters will ask about your background, current compensation expectations, and experience working on production systems or marketplace-style platforms.

Online Coding Assessment (OA)

Candidates who pass the screen receive a 60-minute eBay coding assessment via CodeSignal. This eBay SWE assessment tests your ability to solve algorithmic problems using clean, efficient code. Expect a mix of data structures (arrays, trees, graphs) and logic-based questions under time pressure. Strong fundamentals and time management are key to succeeding in this round.

Technical Phone/Virtual Rounds

These are two back-to-back, 45-minute interviews focused on live problem solving. You’ll face real-world eBay coding interview questions covering topics like string manipulation, dynamic programming, or hash maps. Interviewers assess not only correctness, but also your approach, code quality, and ability to debug on the fly.

On-Site / Loop

Finalists are invited to a full loop (virtual or in-person), which includes four rounds: a coding challenge, a system design interview, a behavioral round, and a 1:1 with the hiring manager. These interviews dive deeper into your technical decision-making, communication style, and product engineering mindset.

Feedback & Offer

After the loop, eBay conducts a cross-panel debrief. Bar-raisers participate in calibrating feedback and aligning levels. If you pass, the recruiter follows up with compensation details, team fit, and start date discussions.

Behind the Scenes: Feedback is usually compiled within 24–48 hours. Bar-raisers help ensure consistency across teams, and hiring managers weigh in on long-term growth potential, not just test performance.

Differences by Level: More senior candidates (L5+) face a heavier focus on architectural trade-offs and cross-functional leadership. Principal-level engineers may also be asked about mentorship history, influencing roadmaps, and scaling best practices.

The eBay software engineer interview is structured to assess not just your coding skills, but also your product sense, scalability thinking, and ability to collaborate in a fast-moving marketplace environment. Candidates can expect a range of problem types across coding, system design, and behavioral rounds.

Below, we break down the core categories of questions asked—whether you’re applying as a backend developer, full-stack engineer, or platform-focused specialist.

Coding / Technical Questions

Expect to solve questions that test your understanding of data structures, algorithm optimization, and real-time system behavior. These eBay programming interview questions often appear during the HackerRank online assessment (OA), technical phone screen, and live rounds.

Some questions focus on classic data structures like hash maps, heaps, and graphs. Others challenge your ability to optimize for time or memory efficiency. A few may dive into concurrency fundamentals or thread-safe design patterns.

Questions in this section frequently map to patterns found on eBay LeetCode or other technical prep platforms. While familiarity with syntax matters, the interviewer is far more interested in how you walk through edge cases, test scenarios, and optimize step by step.

1. What SQL would you write to return the second-highest salary in the Engineering department—even if multiple people tie for first place?

   A concise solution selects distinct salaries in Engineering, orders them descending, and uses OFFSET 1 LIMIT 1, or applies DENSE_RANK() partitioned by department and filters on rank = 2. Explain edge-case handling when only one unique salary exists and how the DISTINCT keyword prevents duplicate rows from pushing the offset. Follow-up discussion typically explores indexing on (department_id, salary DESC) to ensure the sort step remains in memory on millions of employees. Mastery of “Nth highest” patterns is a quick proxy for analytical rigor.

2. Within every department, list the top three salaries (or fewer if head-count is smaller) along with the employee’s full name. How would you guarantee correct ordering?

   Use DENSE_RANK() OVER (PARTITION BY dept_id ORDER BY salary DESC) and filter rank ≤ 3, then sort the final result by dept_name ASC, salary DESC. Mention why ROW_NUMBER() is insufficient when ties exist, and how including the full name in a concat column meets the “one-column” requirement. A high-quality answer also cites indexing on dept_id and storing salary as NUMERIC(10,2) to avoid rounding drift.

3. How far has each user travelled in total, and how would you return the list ordered by distance descending?

   Aggregate SUM(distance_miles) in rides grouped by user_id, join to users for profile context, and order by the sum DESC. Discuss unit consistency (miles vs. kilometers), NULL-safe summation, and how partitioning rides by ride_date keeps the full-table scan bounded during backfills. Interviewers often ask how you would persist this metric in a materialized view for daily ETL.

4. Create a flight_routes table that stores each unique pair of cities exactly once, regardless of direction (e.g., Dallas ↔ Seattle). What SQL achieves this?

   Select LEAST(origin, destination) as city_a and GREATEST(origin, destination) as city_b from flights, then GROUP BY city_a, city_b and insert into the new table. Explain how the LEAST/GREATEST trick canonicalizes unordered pairs, and why a composite primary key on (city_a, city_b) prevents future duplicates. Good answers also mention incremental upserts when new cities appear.

5. For every user, return TRUE if any of their completed subscription periods overlap; otherwise FALSE. What overlap logic do you use?

   A self-join on identical user_id where a.end_date >= b.start_date and a.start_date < b.start_date detects overlap. Filter to completed rows (end_date IS NOT NULL) and wrap the EXISTS logic in a Boolean projection per user. You should address self-matches (a.id ≠ b.id), timezone normalization, and the index (user_id, start_date, end_date) that converts the inequality join into an efficient range scan.

6. Compute a three-day rolling average of deposit amounts (positive values) for every calendar day, formatted YYYY-MM-DD.

   Begin with a CTE that truncates each created_at to date and filters value > 0. Aggregate total deposits per day, then apply AVG(day_total) OVER (ORDER BY txn_date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) as rolling_avg. Explain why window frames beat correlated sub-queries on large ledgers and how you’d materialize daily aggregates to avoid re-scanning raw events. Also highlight rounding choices and the importance of ordering the final output chronologically.

System / Product Design Questions

In system design rounds, you’re typically asked to design large-scale marketplace features like a real-time auction engine, recommendation system, or search autocomplete service. These eBay system design interview questions focus on how you scale systems, reduce latency, and maintain availability under load.

Interviewers assess your ability to navigate trade-offs: when to use a NoSQL database vs. relational schema, how to shard user data, or where caching will have the most impact. While the end design matters, your approach—how you gather requirements, break down problems, and articulate trade-offs—is just as important.

1. Why is it standard practice to declare explicit foreign-key constraints (instead of a plain BIGINT), and when should you use ON DELETE CASCADE versus ON DELETE SET NULL?

   Foreign-key constraints guarantee referential integrity, let the optimizer assume join correctness, and surface data-quality bugs at write time instead of during analytics. They also unlock cascading actions, deferred checks, and better cardinality estimates that improve query planning on large joins. Use CASCADE when child rows have no business value without the parent—e.g., order-items tied to an order—so deletes stay atomic and storage doesn’t leak. Choose SET NULL when downstream facts (like historical metrics) should survive a soft-delete, but you still want to acknowledge that the parent key vanished. The trade-off is higher insert latency and extra diligence to avoid accidental mass deletes along deep cascade chains.

2. Design a database schema and outline key optimizations for a Tinder-style swiping app.

   Core tables include users, swipes(user_id, target_id, direction, ts), matches(user_a, user_b, ts), and messages. A composite index on (user_id, ts DESC) speeds “recent swipes” look-ups, while an (user_id, target_id) unique index prevents duplicate swipes. Shard swipes by user_id % N to diffuse hot users, and materialize a “pending likes” view to compute mutual RIGHT swipes in O(1). Read-heavy profile cards live in an edge cache (Redis or DynamoDB DAX), and a background job emits push notifications when matches form. Discuss eventual vs. strong consistency for likes, GDPR data deletion, and graceful handling of flash-in-the-pan bot accounts.

3. Architect a notification system for a Reddit-like discussion app—what backend services and data model would you propose?

   Raw events (comment, reply, mention) flow into Kafka, where a stream processor fans them out into notifications(user_id, event_type, ref_id, ts, is_read). Recent notifications (≤ 100 per user) stay in a Redis list keyed by user_id for sub-100 ms mobile pull, while long-tail history persists in a partitioned Postgres table for audit and GDPR export. Deduplication keys (user_id + ref_id + event_type) stop double sends, and a push-service tier batches similar events (“+12 replies”). The design separates storage (cold / hot) from delivery, supports at-least-once semantics, and allows backfill replays by consuming Kafka from an older offset.

4. Model an airline network that tracks hubs, non-hub airports, and weighted routes so you can compute the lowest-fuel hub-to-hub path that passes through non-hubs. What would the data model look like?

   Use airports(id, name, lon, lat, is_hub) and routes(src_id, dst_id, fuel_cost) to represent an undirected, weighted graph. Index (src_id, fuel_cost) to accelerate Dijkstra’s algorithm, and store great-circle distance in PostGIS to derive heuristic bounds for A*. Pre-compute hub-to-hub baseline paths in a nightly Spark job that outputs hub_paths(hub_a, hub_b, path, total_cost) so real-time queries need only consult one row. When new routes arrive, a priority-queue incremental update recalculates affected paths instead of re-running the full graph search. The schema is normalized for accuracy yet cheap to replicate into a columnar store for ad-hoc planning.

5. Design a real-time auction engine for eBay’s Promoted-Listing ads that scales to 200 K QPS at sub-50 ms latency.

   Requirements: second-price logic, per-seller spend caps, and five-nines availability. A stateless bidding tier (Go or C++) keeps in-memory campaign data loaded from a sharded DynamoDB table; bids stream through Kafka, and a gRPC budget service atomically decrements winning bids. Results are cached for one minute to reduce repeat look-ups, while a write-ahead Redis log allows replay after node failure. Trade-offs revolve around strict budget consistency (slower) versus eventual reconciliation (faster).

6. Outline a multi-tenant recommendation pipeline that serves “similar-items” widgets on product pages within 100 ms.

   Nightly Spark ML jobs compute item embeddings and write top-K neighbor lists to RocksDB shards keyed by item_id hash; edge servers mount the shard locally. Cache misses route to a gRPC recall service backed by Cassandra. Real-time click feedback streams into a feature store, and an hourly LightGBM rescoring job updates CTR probabilities. Discuss item cold-start with content features, A/B guardrails, and rolling shard deployments to avoid cache stampedes.

7. Create a fault-tolerant search-autocomplete service that ingests 1 M catalog updates per day.

   An ElasticSearch cluster handles prefix queries, while Redis stores the hottest 50 K terms with LRU eviction. Debezium CDC streams catalog changes into an indexing pipeline that bulk-updates Elastic with zero-downtime blue-green switches. Shard indices by language-region to localize suggestions, and use speculative dual reads to beat tail latency. Explain disaster recovery and how you’d monitor index freshness.

8. Design an anomaly-detection dashboard that flags seller refund spikes within 10 minutes.

   Refund events flow through Kinesis, aggregate in Flink into seller-hour buckets, then compute z-scores against a 30-day EWMA baseline stored in Redis. Alerts with z > 4 trigger PagerDuty and write to refund_alerts. A nightly job retrains decay rates based on concept-drift metrics. Emphasize sliding-window state management and alert de-duplication.

9. Propose a hybrid HTAP schema to power real-time buyer–seller messaging plus weekly analytics.

   Use TiDB for OLTP (messages table) and TiSpark to roll up daily_msg_counts. Secondary indexes on (thread_id, ts) support fast scrollback; TTL policies purge messages older than 90 days to control storage. Changefeeds replicate into Redshift for NLP sentiment analysis. Discuss write amplification vs. analytical flexibility and how to handle back-pressure under viral chat surges.

10. Sketch a data-quality framework that blocks duplicate listings, missing category tags, and price outliers before nightly ETLs publish to production.

    Define expectation rules in DBT or Great Expectations, log results in dq_run_results(run_id, rule_id, status), and fail-fast if any critical rule breaches. Non-critical warnings proceed with a Slack alert. Integrate the framework with Airflow so DAGs register or deprecate rules via YAML, ensuring governance keeps pace with schema evolution.

Behavioral & Culture-Fit Questions

eBay places strong emphasis on ownership, collaboration, and customer impact. Behavioral interviews often explore how you’ve navigated past engineering challenges—particularly when working across teams or responding to incidents in production.

Be ready to share stories about launching features at scale, improving a broken process, or mentoring peers.

1. Describe a data-driven project you worked on. What challenges did you face and how did you overcome them?

   Interviewers want a concise STAR story: Situation (business context), Task (your role), Action (specific technical steps—e.g., refactoring flaky ETL, optimizing queries, or mentoring junior devs), and Result (quantified impact). Highlight concrete hurdles such as legacy schemas, shifting requirements, or scale surprises, then focus on how you unblocked the team—think data-quality monitoring or incremental rollouts that reduced production incidents by X %.

2. What techniques have you used to make complex data more accessible to non-technical partners?

   Strong answers mix tooling (self-serve dashboards, Looker/Metabase, automated data dictionaries) with product thinking (plain-language KPIs, in-app tooltips). Emphasize user interviews, iterative UX tweaks, and the business lift—e.g., finance analysts cut ad-hoc SQL tickets by 40 % after you rolled out a metrics layer.

3. What are your three biggest strengths and weaknesses, and what feedback would your current manager give you?

   Choose role-relevant strengths (e.g., “debugging distributed systems”) backed by evidence, then pick developmental areas you’re actively improving (say, “delegating tasks sooner”) and describe the plan—mentorship, time-boxing, or reading groups. Referencing real manager feedback shows self-awareness and coachability.

4. Tell us about a time you struggled to communicate with stakeholders. How did you bridge the gap?

   Outline the mis-alignment (different terminology, conflicting priorities), the tools you used (white-boarding, data prototypes, weekly syncs), and the result—consensus on API contracts or a revised roadmap. Highlight empathy and iterative validation, qualities eBay values in cross-functional SWE roles.

5. Why do you want to work at eBay, and what makes you a strong fit for this role?

   Map eBay’s mission (“enabling economic opportunity”) to your experience—perhaps in marketplace payments, search relevance, or seller tools. Then tie specific job requirements to your track record: “I’ve scaled Kafka pipelines to 1 M msg/sec, which aligns with eBay’s real-time listing analytics.”

6. How do you juggle multiple deadlines while keeping code quality high?

   Discuss prioritization frameworks (MoSCoW, impact–effort), sprint rituals, test automation, and early stakeholder demos that surface blockers fast. Mention tooling—CI pipelines, static analysis—to prove you don’t sacrifice reliability for speed.

7. Describe a situation when you proactively spotted a performance bottleneck in production and drove a fix before it became a customer issue.

   Interviewers look for ownership beyond assigned tickets. Walk through how you detected early signals (dashboards, error rates), framed the risk, rallied the team, and shipped an optimized index or cache. Quantify the outcome—latency dropped 60 %, support tickets avoided.

8. Tell us about a time you mentored a junior engineer who was struggling with a core SWE skill (e.g., code reviews or system design). What was your approach and what changed?

   Highlight active listening, setting incremental goals, and pairing sessions. Conclude with measurable growth—PR turnaround time improved, or the mentee led their own design doc—demonstrating your potential as a multiplier on eBay’s engineering culture.

To stand out in eBay’s interviews, you’ll need both technical strength and the ability to reason through product-driven trade-offs. Here’s how to sharpen your edge.

Understand the Role and Culture

Before diving into problems, spend time understanding the team and the products they own. eBay is an engineering-driven company, but each solution is tightly tied to buyer and seller experiences. Tailor your stories to show how your work has impacted end-users or internal tools—especially at scale.

Practice the Common Question Formats

eBay’s interviews break down to roughly 70% coding, 20% system design, and 10% behavioral. Focus your prep time accordingly. For coding, stick to medium-hard algorithm problems with practical applications. For design, walk through systems with real-time constraints and horizontal scaling challenges.

Narrate Your Thought Process

In both phone screens and onsite rounds, eBay interviewers expect collaboration. Don’t code silently—walk through your assumptions, flag potential trade-offs, and respond to interviewer nudges in real time. This simulates eBay’s engineering culture, where design and review are highly conversational.

Start Brute Force, Then Optimize

A common trap is staying too long in analysis paralysis. eBay engineers are trained to start with what’s simple and improve iteratively. Show your ability to think in passes—first a naive solution, then a refinement to hit constraints or improve readability.

Use Mock Interviews and Get Feedback

Nothing beats real-time simulation. Try Interview Query’s eBay coding interview questions in timed mode or run peer mock loops. Practicing aloud helps you tighten your narrative and recognize common pitfalls—whether in indexing, recursion, or explaining trade-offs clearly.

What Is the Average Salary for an eBay Software Engineer?

The eBay software engineer salary varies depending on level, location, and function (backend, frontend, infrastructure). Compensation typically includes base pay, annual bonuses, and RSU packages. Entry-level engineers receive a balanced mix of cash and equity, while senior engineers may have stock-heavy packages and higher performance bonuses.

You may also see this listed in peer forums under eBay SWE salary or negotiated packages on eBay SDE salary LeetCode threads.

Does eBay Use LeetCode-Style Problems?

Yes. Both online assessments and live interviews often draw from eBay LeetCode-style questions, especially for roles in backend and full-stack engineering. Expect variants on dynamic programming, two-pointer methods, graph traversal, and concurrency primitives.

How Are Levels Structured for Software Engineers at eBay?

The eBay software engineer levels range from entry-level (L3) to staff and principal roles (L6–L7). Each level corresponds to technical scope, leadership expectations, and impact radius—ranging from individual contributor problem-solving to architectural planning across multiple teams.

Succeeding in the eBay software engineer interview isn’t just about nailing a few coding problems—it’s about showing you can think critically, collaborate effectively, and build systems that scale with millions of users. From breaking down ambiguous product asks to iterating on real-time performance bottlenecks, the strongest candidates know how to move from brute-force to optimized code while clearly communicating their decisions.

Whether you’re preparing for your first round or polishing for an onsite, we recommend starting with a timed session in our AI Interviewer, followed by a Mock Interview to get structured feedback. You can also explore our Software Engineer Learning Path to build up your skillset across SQL, systems, and design patterns. And if you need inspiration? Check out Jayandra Lade’s success story—how he cracked the tech interview loop and landed his dream role at a top-tier company. Prepare smart. Practice often. Land the offer.