Ebay's Data Analyst interviews mix an early SQL screen or online test with behavioral manager rounds, and onsite stages that lean on either e-commerce SQL/Excel scenarios or machine learning fundamentals like decision trees and random forests.
$108K
Avg. Base Comp
$155K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
For Ebay's Data Analyst role, the earliest stage looks different depending on the candidate. One intern candidate was given a CodeSignal SQL problem described as medium-to-hard difficulty directly inside a hiring manager screen, alongside questions about whether they preferred a business-facing or technical track. A separate full-time candidate instead faced an online test with no phone screen first, followed by a manager interview centered on behavioral questions like describing a challenging project and how they collaborated with colleagues.
Further along, the technical content varies by candidate. One rejected full-time candidate's onsite included a peer interview, a roughly 25-minute technical round covering advanced SQL and Excel questions framed around e-commerce scenarios, and a second manager interview; that candidate felt the team expected deeper database ownership than the job posting implied. A different candidate who received an offer instead reported technical rounds built around machine learning fundamentals, especially decision trees and random forests, plus a take-home project simulating real team data problems. That same candidate described early rounds as relaxed and collaborative but found the final hiring manager round noticeably colder and more rapid-fire in tone.
Because only a handful of accounts are available, treat the exact mix of SQL, Excel, and ML questions as variable by team rather than a fixed script, and prepare broadly across both SQL fundamentals and core ML concepts rather than assuming one path.
Synthesized from 4 candidate reports by our editorial team.
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Real interview reports from people who went through the Ebay process.
Hiring manager from UK. First focused on understanding my fit for the role - I gave a long answer to the 'tell me about yourself' question and focused on highly relevant details from past experiences that 100% aligned with the role. Next the HM presented a question on 'how to choose an audience to show a specific carousel?' We do not want to target everyone, so how would you go about targeting and identifying an interested audience.
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Topics based on recent interview experiences.
Featured question at Ebay
Write a query to get the total three-day rolling average for deposits by day
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| Button AB Test | |
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| Sample Size Bias | |
| Bias vs. Variance Tradeoff | |
| The Longest Journey | |
| Seller Type Modeling | |
| String Palindromes | |
| New UI Effect | |
| Relational Migration | |
| Free Shipping Mention Test | |
| Decreasing Tech Debt | |
| A/B Test Power Size | |
| Fast Food Database | |
| Marketing Dollar Efficiency | |
| Statistically Significant Test | |
| Bias Variance Tradeoff | |
| Scaling Up Recommender | |
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Synthesized from candidate reports. Individual experiences may vary.
One intern candidate had their SQL proficiency discussed during a hiring manager screen and then completed a single CodeSignal SQL problem described as medium-to-hard, LeetCode-style difficulty. A separate full-time candidate instead completed an online test as the very first step, with no phone screen beforehand.
The intern candidate also had a second, more conversational screen covering their background, what they wanted from their next role, and where they saw themselves in five years, with time left for their own questions. A full-time candidate instead described a manager interview focused on behavioral questions, including describing the most challenging program they worked on and how they collaborated with colleagues at their last job.
Technical depth differed across reports. One full-time candidate's onsite technical round (about 25 minutes) covered SQL scenarios tied to e-commerce use cases plus Excel questions, with the team reportedly looking for someone able to rebuild their database. Another full-time candidate said technical rounds instead focused on machine learning fundamentals, particularly decision trees and random forests, including when to use each and their trade-offs.
One candidate who received an offer completed a take-home project described as simulating actual work challenges the team faces. This step was not mentioned by other candidates, so it may not apply to every hiring cycle.
For the full-time role, one candidate's onsite included a peer interview (about 30 minutes), a technical interview, and a second manager interview. Another full-time candidate who received an offer described earlier rounds as relaxed, including an office tour and team introductions, but found the final hiring manager round unexpectedly cold and rapid-fire compared to the rest of the process.
