eBay Data Engineer Interview Guide: Process, Sample Questions & Preparation

Joining as an eBay data engineer means operating at one of the largest data scales in global commerce. From ingesting terabytes of marketplace interactions to optimizing distributed pipelines for fraud detection and real-time recommendations, data engineers at eBay are core to how the platform runs—and evolves.

Role Overview & Culture

A day in the life of an eBay Data Engineer involves owning end-to-end data infrastructure—from writing Spark and Flume jobs to managing Kafka topics and deploying Flink-based real-time analytics layers. Teams ingest massive volumes of behavioral and transactional data across buyers, sellers, listings, and logistics. Engineers collaborate cross-functionally with ML teams, Product Analysts, and infra stakeholders to ensure data accuracy, latency, and scalability.

Culturally, eBay is rooted in customer-first thinking, continuous experimentation, and bottom-up ownership. This shows up in how data teams are structured—small, empowered pods that make infrastructure choices to match business needs, not the other way around.

Why This Role at eBay?

The scale of impact is huge—billions of events flow through the platform each day. You’ll work with a modern tech stack that includes Snowflake, Apache Flink, Airflow, and Kafka, supporting everything from dashboarding to real-time user segmentation. The data engineer career path at eBay is well-defined, moving from DE II to Senior and eventually to Staff, where you may own multi-org platform initiatives.

To land the job, you’ll face a multi-stage eBay data engineer interview focused on ETL design, big-data coding, and cross-team collaboration.

The eBay data engineer interview process is built to test your ability to code at scale, architect robust pipelines, and communicate clearly across teams. Expect four core stages, each with distinct focus areas.

Application & Recruiter Screen

The process begins with a recruiter screen where your background, resume fit, and compensation expectations are discussed. You may also get early insight into the specific domain (e.g., Risk, Ads, Infrastructure) you’re being considered for.

Technical Screen

This round is typically a 45-minute virtual interview involving a Python or Scala coding problem and a set of SQL queries. Questions often mimic Spark or BigQuery-style data processing. You might be asked to manipulate nested JSON, filter large event logs, or join multi-partition datasets with performance constraints in mind.

On-Site / Virtual Loop

The main interview loop includes:

• A live coding round focused on distributed systems
• A system design interview where you’ll sketch a pipeline from ingestion to storage
• A behavioral panel evaluating collaboration and communication
• A hiring manager conversation to align on expectations and growth trajectory

During this stage of the eBay data engineer interview, interviewers are especially attentive to your understanding of data trade-offs: latency vs. throughput, partitioning strategies, schema evolution, and debugging edge cases in production.

Feedback & Hiring Committee

After the loop, eBay gathers feedback from all interviewers within 24 hours. A hiring committee then meets to evaluate your performance, determine level calibration (e.g., E4 vs. E5), and finalize offer terms.

Behind the Scenes: eBay uses structured feedback forms and bar-raiser interviewers to ensure consistency and fairness in decision-making.

Differences by Level: Junior engineers typically skip the leadership interview and focus more on core coding and SQL. Senior candidates are expected to handle platform-scaling design and communicate roadmap-level thinking.

eBay’s technical interview questions are designed to test your engineering depth and system design intuition under real-world data constraints.

Coding / Technical Questions

A typical prompt might be: “Write a Spark job to deduplicate and aggregate seller events by hour.” These questions test your comfort with distributed joins, window functions, and handling high-volume data efficiently. Strong answers walk through brute-force logic first, then discuss optimizations like partitioning, bucketing, or using broadcast joins. Expect to justify trade-offs for memory, I/O, and scalability.

This is where eBay data engineer candidates often shine or stumble—clean, modular code that scales is a must.

1. Write a SQL query to sample a truly random row from a 100-million-row table without throttling the database.

   Explain two production-safe patterns: (a) if id values are dense, generate FLOOR(RANDOM()*max_id) and grab the first row ≥ that id with an indexed lookup, or (b) use a pre-materialized “reservoir” table that’s refreshed nightly with 1 M random keys so the online query is just ORDER BY RANDOM() LIMIT 1. Stress why ORDER BY RANDOM() on the base table forces a full sort and will lock up a live replica. Mention hash-mod sharding for MySQL Aurora or TABLESAMPLE BERNOULLI(0.0001) in Redshift, noting their caveats. Tie the use case to eBay: product-photo moderation teams often need random listing slices for QA without slowing the main commerce cluster.

2. Retrieve the last transaction of each day, returning its id, timestamp, and amount.

   Demonstrate ROW_NUMBER() OVER (PARTITION BY DATE(created_at) ORDER BY created_at DESC) = 1, then order the result ascending by created_at. Highlight indexing (created_at DESC) to turn the window sort into a range scan, critical when the finance ledger hits billions of rows. Discuss why date-truncing in the partition key is safer than string casting when the warehouse stores UTC. This pattern powers end-of-day cash-balance snapshots feeding eBay’s revenue reconciliation jobs.

3. Report total distance traveled by each user, in descending order.

   Join users to rides, sum distance per user_id, COALESCE missing rides to zero, and sort. Call out that pre-aggregating ride data into a partitioned fact table speeds nightly ETL and prevents hot-key scans on heavy users. Mention adding an INDEX(user_id, distance) or clustering on user_id for columnar stores. The resulting mileage feeds loyalty-tier logic or carbon-offset calculators.

4. Identify customers who placed > 3 orders in both 2019 and 2020.

   Aggregate orders by customer_id and YEAR(order_date), filter COUNT(*) > 3, pivot years with HAVING SUM(year=2019)>0 AND SUM(year=2020)>0. Alternatively use an INTERSECT of two subqueries for readability. Emphasize why a composite (customer_id, order_date) sort key accelerates the grouping. This cohort query seeds retention dashboards and recommendation-model training.

5. Return the top three highest salaries per department, listing employee full name, department, and salary.

   Apply DENSE_RANK() OVER (PARTITION BY dept_id ORDER BY salary DESC) and filter rank <= 3. Discuss memory trade-offs of window-function spill when departments are skewed and suggest pre-sorting with DISTKEY(dept_id) in Redshift. Note that for compliance reporting eBay masks PII: build the full name at query time rather than storing it denormed.

6. Create a January 2020 histogram of comments per user, one-comment bin width, plus a “0” bucket for users with zero comments.

   Generate a numbers table (0-N) with generate_series, left-join to aggregated COUNT(*) results, and default missing counts to zero. Explain why storing pre-calculated histograms in a metrics table speeds BI dashboards. Also touch on log reprocessing to ensure comment deletions are back-filled; otherwise the “0” bucket is inflated.

7. Select the second-highest salary in Engineering, skipping ties at the top.

   Use DISTINCT salary then ORDER BY salary DESC OFFSET 1 LIMIT 1, or DENSE_RANK() where rank = 2. Point out why sub-queries on salary avoid accidentally picking two co-workers who share rank 1. Such queries often power compensation-band analytics during talent-market benchmarks.

8. Determine if there is a path through a maze grid from start to target, returning a Boolean.

   Describe BFS with a queue of (x,y) positions and a visited set; the algorithm is O(rc) and detects reachability without requiring the shortest path. Emphasize memory efficiency for large grids by storing visited as a bitset. Data engineers building ETL state-machines sometimes face analogous graph-reachability checks on DAG dependencies.

9. Find the minimum time steps to exit a building grid where each room has exactly one unlocked door.

   Because out-degree = 1, the building forms a directed graph of edges; BFS still works, but you can prune by noting that revisiting a cell means a loop, so terminate early. Return −1 if the southeast corner is unreachable. Explain why a sparse adjacency list is more cache-friendly than a full 2-D matrix on very large buildings.

10. Compute a recency-weighted average salary from an ordered list of yearly salaries, heavier on recent years.

    Show a Python or SQL window solution: assign linear weights 1…n, compute SUM(salary*weight)/SUM(weight), and round to two decimals. Note numerical-overflow guards when salaries are high and n large. This pattern generalizes to time-decayed CTR features in eBay’s recommendation pipelines.

11. Find the top five paired products most frequently bought together.

    Self-join transactions on user_id where product_id₁ < product_id₂, group by the pair, count co-occurrences, and order by COUNT(*) DESC LIMIT 5. Stress creating a compound index (user_id, product_id) to avoid the quadratic blow-up, and pre-filter to users with ≤ 100 items per checkout to keep the join tractable. The resulting association rules feed cross-sell banners.

12. Given slope-intercept tuples, find which lines intersect within a specified x-range.

    Iterate pairs, solve m₁x + b₁ = m₂x + b₂, test whether x lies in range, and collect intersecting pairs. Highlight floating-point tolerance and avoid redundant pair checks via combination indexing. While algorithmic, similar geometry checks appear in eBay’s image-processing ETL (e.g., bounding-box overlaps).

13. Select a random element from an unbounded number stream with equal probability using O(1) space.

    Outline reservoir-sampling: keep choice and increment count; for each new element generate rand < 1/count to decide replacement. Prove uniformity via induction. Stress how this feeds unbiased validation slices for continuous-integration model retraining without storing full logs.

System / Pipeline Design Questions

Design questions assess how you think about architecture. You might be asked, “Design a near-real-time pipeline to enrich fraud signals.” Interviewers want to hear your reasoning around ingestion frameworks (Kafka vs. Flume), processing modes (Flink vs. Spark Streaming), and storage choices (Parquet, Delta, etc.). Highlight your ability to balance processing guarantees (exactly-once vs. at-least-once), manage schema evolution, and deliver on SLA constraints.

1. How would you create or modify a schema that tracks a customer’s address history—including who moves in afterward?

   Model addresses as a slowly changing dimension: a surrogate address_id table stores the immutable street data, and a fact-like address_history table captures customer_id, address_id, move_in_date, and move_out_date (NULL if current). Add a unique constraint on (address_id, move_in_date) to prevent overlaps and a forward pointer (next_tenant_customer_id) if you need fast “who moved in after” look-ups. Index {customer_id, move_in_date DESC} for latest-address queries, and partition the history table yearly once it grows. This design supports time-travel analytics, legal audits, and simple joins back to the customer dimension without duplicating street strings.

2. Design a relational schema for a ride-sharing app that records trips between riders and drivers.

   Core tables: riders, drivers, vehicles, and trips. The trips fact holds foreign keys to rider, driver, vehicle, pick-up / drop-off geohash, fare, status, and timestamps (requested, accepted, completed). A many-to-many driver_availability table stores driver status windows to support dispatch queries. Use composite indexes on (pickup_geo, requested_ts) for surge heat-maps and (driver_id, status) for driver-utilization dashboards. Soft-delete flags plus append-only write patterns keep historical data intact for ML ETA training.

3. How would you configure storage and indexing when a video platform’s Blob store exceeds 10 million videos?

   Separate metadata and binary content: store video files in object storage (S3/GCS) with deterministic keys, and keep a lightweight videos_meta table in a columnar or row store (title, uploader_id, upload_ts, duration, content_type). Create covering indexes on (uploader_id, upload_ts DESC) and full-text search on title to keep UI queries fast. Leverage tiered storage: cold videos live on infrequent-access buckets while hot-cache thumbnails sit in CDN. Blob URLs include an MD5 checksum to guarantee integrity; the database stores only the checksum and the signed URL, reducing row size.

4. Sketch a star-schema data warehouse for a new online retailer.

   Fact table: sales_fact with keys to date_dim, customer_dim, product_dim, store_dim, and measures like revenue, quantity, discount. Dimensions carry slowly changing attributes—e.g., product_dim tracks price_effective_from / to, category hierarchy. Index fact on composite (date_key, product_key) for top-seller reports; partition by date_key for fast roll-ups. Offload clickstream into a separate session_fact that can later join via customer_key. This star model simplifies Tableau cubes while supporting granular funnel analysis for eBay-style merchandising.

5. How would you remove duplicate products—like “iPhone X” vs. “Apple iPhone 10”—in a very large catalog?

   Build a two-phase pipeline: first, offline blocking rules cluster candidates using text normalization (lowercase, stemming, model number regex), brand match, and category. Second, apply a pairwise similarity model (TF-IDF cosine or Siamese BERT) and threshold to flag duplicates. Store clusters in a product_canonical_map table with canonical_id and confidence. Expose an admin tool for manual review of borderline cases. Downstream queries join through canonical_id, ensuring inventory counts and search facets collapse duplicates without deleting seller listings.

6. Design a schema to capture client click data for web-app analytics.

   Emit each click as an event into Kafka, land in Parquet partitions by UTC date-hour, and stream into a click_events table: (event_id, user_id, session_id, element_id, page_url, ts, device, referrer). Keep a surrogate session_dim table to roll clicks into page-view metrics quickly. Use big-int surrogate keys for element_id to avoid long CSS selectors in the fact. Add a low-cardinality country_code column encoded with ZSTD to speed geo aggregation; Bloom filters on user_id accelerate user replay.

7. Why explicitly declare foreign-key constraints instead of plain BIGINT fields, and when use CASCADE vs. SET NULL?

   Foreign keys enforce referential integrity, catch orphan inserts early, and allow cost-based optimizers to infer join cardinality. They can also cascade deletes but should be used cautiously: employ ON DELETE CASCADE for true child-records (order_items) where a parent delete must remove children; prefer ON DELETE SET NULL when history should remain (e.g., user_id on reviews after account deletion). Disable FK checks on bulk loads but re-enable afterward for ongoing safety.

8. Design a blogging-platform schema with users, posts, comments, and tags.

   Tables: users (PK user_id), posts (post_id, author_id FK, title, body, published_ts), tags (tag_id, name UNIQUE), post_tags (post_id, tag_id composite PK), and comments (comment_id, post_id, author_id, parent_comment_id NULL, body, created_ts). Apply a self-referencing FK on comments.parent_comment_id for threading, and a partial index WHERE published=true on posts for feed queries. Text-search GIN index on body accelerates search. Soft-delete is_deleted allows GDPR wipe without breaking FK chains.

9. Design a relational schema optimized for a Tinder-style swiping app.

   Core tables: profiles, swipes (swiper_id, swiped_id, direction [LIKE/SKIP], ts), and matches (user1_id, user2_id, matched_ts). Create a composite unique constraint on matches to avoid duplicates (LEAST(), GREATEST() trick). Hot-path reads— “who should I see next?”—query a Redis cache keyed by swiper_id that stores candidate queues prefiltered by geo and preferences, refreshed via Spark pipelines every few minutes. Index swipes on (swiper_id, ts DESC) for fast history scans and on (swiped_id, direction) to compute likes-received counts.

10. Outline a database schema for a Yelp-like restaurant-review system, including constraints and field descriptions.

    users table stores profile info; restaurants holds location, cuisine, owner_id, and avg_rating cached for quick sort. reviews (review_id, user_id FK, restaurant_id FK, rating 1–5, body, created_ts, updated_ts) with a unique (user_id, restaurant_id) constraint ensures one review per user. review_photos links multiple images to a review via photo_url, and restaurant_tags supports facet search. Add CHECK rating BETWEEN 1 AND 5 and ON UPDATE CASCADE so edited reviews recompute aggregates through triggers. Partition reviews by restaurant_id hash for write scalability, while secondary indexes on (restaurant_id, created_ts DESC) power paginated review feeds.

Behavioural / Culture-Fit Questions

eBay places high value on collaboration and ownership. Behavioral questions often center on how you resolved an incident, ensured data quality, or partnered with downstream ML or analytics teams. Expect prompts like “Tell me about a time you caught a silent data bug in production” or “Describe a situation where misaligned schemas caused downstream failures.” Use STAR format and emphasize impact—especially where your work unblocked others.

1. Describe a data project you worked on. What were some of the challenges you faced?

   Pick a pipeline that moved from batch to streaming—for example, migrating seller-event logs to Kafka + Flink. Call out a technical hurdle (late-arriving events caused watermark skew) and an organizational hurdle (marketing needed hour-level metrics while finance needed day-level ledger locks). Explain how you solved both with schema-versioning and tiered aggregate tables, then share the impact (20 % fresher dashboards, 40 % less re-processing cost).

2. What are effective ways to make data more accessible to non-technical people?

   Discuss semantic layers in Looker, data-quality badges, and self-serve notebooks that read from governed Iceberg tables. Mention autogenerated data dictionaries and Slack bots that field SQL snippets. Highlight how these tools lowered ad-hoc ticket volume by half and empowered category managers to pull GMV by seller tier without engineering help.

3. What would your current manager say about you—strengths and areas to improve?

   Choose strengths that map to eBay’s scale—e.g., “obsession with idempotent backfills” and “proactive cost-tracking on Redshift clusters.” Offer one growth area such as “delegating terraform changes,” then describe concrete steps you’re taking (pair-review checklists, runbooks). Back claims with metrics (zero failed backfills in two quarters).

4. Talk about a time you had trouble communicating with stakeholders. How did you overcome it?

   Use a STAR story where product wanted near-real-time analytics but SRE warned of CPU spikes. Explain how you visualized cluster utilization, proposed micro-batching, and wrote an RFC everyone signed off on. Result: latency cut from 24 h to 30 m with no pager alerts.

5. Why do you want to work with us?

   Tie your passion for marketplace data and petabyte-scale warehousing to eBay’s mission. Reference eBay’s adoption of open-table formats (Iceberg) and how your experience optimizing S3 + Athena costs can accelerate the Ads and Payments data domains.

6. How do you prioritize multiple deadlines and stay organized?

   Describe an impact-versus-effort matrix tied to quarterly OKRs, Jira swim-lanes color-coded by SLA risk, and daily stand-ups that surface blockers. Mention automated Airflow DAG health checks and Terraform plans that gate prod merges. Give an example where this system surfaced a P1 schema drift while still hitting a GDPR export deadline.

7. Tell me about a time you caught a data-quality issue before it hit production dashboards. What safeguards did you have in place?

   Interviewers want defenses in depth: perhaps Great Expectations tests on row counts, plus Canary queries that diff new partitions against yesterday’s. Explain how the alert let you roll back a faulty CDC stream and prevented incorrect GMV from reaching the CFO’s report.

8. Describe how you mentored a junior engineer or analyst—what was the challenge and the outcome?

   Maybe you guided an intern through building a partition-evolution script; detail code reviews, architecture white-boarding, and how the mentee’s PR went live reducing scan costs 15 %. Emphasize multiplying team capacity and fostering a culture of learning—highly valued in eBay’s collaborative data org.

Preparing for the eBay data-engineering interview means sharpening both your technical depth and architectural judgment. It’s not just about writing performant code—it’s about building resilient systems at scale and articulating your decisions clearly.

Master eBay’s Scale Numbers

Before jumping into prep, ground yourself in eBay’s scale. Think in gigabytes-to-terabytes per day, partitioning schemes, event latency budgets, and SLA targets for downstream consumers. Many design prompts will assume this level of context—especially in real-time analytics or fraud detection domains.

Practise the Question Mix

Most candidates encounter roughly 50% coding (PySpark, SQL, data-structure questions), 30% design (streaming vs. batch, data models), and 20% behavioral (ownership, communication, incident response). Focus your time accordingly, and treat each practice session like a simulation of the real process.

Mock the Technical Screen

The technical screen is a performance gate—prepare with 45-minute timed IDE sessions using LeetCode (medium-to-hard) and Spark playgrounds like Databricks or AWS Glue Studio. Practise deduping, joins, window functions, and writing memory-aware transformations with comments. Clean code = a strong first impression.

Think Out Loud

Interviewers care as much about how you think as what you build. Always articulate your partitioning logic, fault tolerance plan, and monitoring considerations. Highlight trade-offs like shuffle cost vs. reusability, or throughput vs. consistency. These nuances elevate you above brute-force problem solvers.

Collect Feedback Early

When practicing system design questions, show your diagrams to a peer, mentor, or coach. Clarity, pacing, and diagram structure matter—especially when describing data lineage, backfills, or Kafka-Flink-Snowflake flows under a time constraint.

What Is the Average Salary for an eBay Data Engineer?

The eBay data engineer compensation package typically includes base salary, performance bonus, and equity through RSUs. While eBay doesn’t publicly break down salaries, benchmark data suggests that total comp ranges vary significantly by level—expect higher packages for Senior and Staff-level roles with cross-team platform scope.

How Much Spark vs. SQL Appears in the Interview?

Expect a healthy mix. While foundational SQL skills (joins, CTEs, aggregates) are tested in all loops, Spark questions dominate the coding and design rounds. Candidates should prepare to write PySpark transformations, explain execution plans, and optimize partitioning and memory use. Familiarity with UDF pitfalls and broadcast joins can also come up.

The key to success in the eBay data engineer interview is pairing distributed systems design expertise with sharp, confident communication. Whether you’re optimizing Spark jobs or whiteboarding a Flink-to-Snowflake ingestion pipeline, your ability to explain trade-offs and drive resilient decisions is what sets you apart. For next steps, visit our broader eBay Interview Questions & Process hub, or explore role-specific guides for Machine Learning Engineers and Data Analysts.

Want to simulate the pressure? Book a mock interview, try our AI Interviewer, or challenge yourself with the Data Engineer Learning Path. And don’t miss Simran Singh’s success story—after 4,200 job applications, she landed her dream role with the help of Interview Query. Let this be your breakthrough moment.