EAB Data Engineer interview typically runs 4 rounds: aptitude test, phone screening, live coding and behavioral interview, final data analytics round. The process took about a few weeks and was structured, with a mix of screening and hands-on practical work.
$80K
Avg. Base Comp
$100K
Avg. Total Comp
4-5
Typical Rounds
2-4 weeks
Process Length
Our candidates report that EAB cares less about flashy algorithms and more about whether you can work like a dependable data engineer in a real team. A recurring theme is solid SQL fundamentals with practical judgment: joins, unions, group by, having, and especially being able to explain the differences between ROW_NUMBER, RANK, and DENSE_RANK without hand-waving. We’ve also seen that the company uses simple filters early on, like basic pattern recognition and math, to quickly separate general problem-solvers from people who are truly comfortable with data work.
What makes EAB a little different is the amount of applied debugging and analysis baked into the process. One candidate was asked to correct a DDL query for inserting values into rows, which suggests they want engineers who can spot issues in production-style code, not just write clean queries from scratch. Another round used two datasets and asked for graphs plus interpretation, so the signal isn’t only technical correctness — it’s whether you can translate data into a clear narrative and explain your reasoning as you go. For this role, the strongest candidates seem to be the ones who can move smoothly between SQL mechanics, production awareness, and thoughtful analysis.
Synthetized from 1 candidates reports by our editorial team.
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Topics based on recent interview experiences.
Featured question at Eab
How would you answer when an Interviewer asks why you applied to their company?
| Question | |
|---|---|
| Merge Sorted Lists | |
| Prime to N | |
| Largest Salary by Department | |
| Find the Missing Number | |
| Top 5 Turnover Risk | |
| The Brackets Problem | |
| Flight Routes | |
| Hurdles In Data Projects | |
| String Mapping | |
| Missing Housing Data | |
| Find Duplicate Numbers in a List | |
| Flatten JSON | |
| Target Indices | |
| Swap Variables | |
| Move Zeros Back | |
| New Resumes | |
| Total Transactions | |
| Cyclic Detection | |
| Slow SQL Query | |
| Binary Tree Conversion | |
| Valid Anagram | |
| String Palindromes | |
| Targeted sum | |
| Equal Binary Subarrays | |
| Double Card Value | |
| Find Square Root | |
| Your Strengths and Weaknesses | |
| Client Solution Pushback | |
| Stakeholder Communication |
Synthesized from candidate reports. Individual experiences may vary.
The process starts with an online application. Based on the experience, this appears to be the first filter before any direct contact from the recruiting team.
Candidates complete an aptitude test focused on basic pattern recognition and math. It seems to function as a quick initial screen rather than a deep technical assessment.
This round is mostly resume-based and conversational, with some SQL basics such as joins and unions. The goal is to confirm background fit for the Data Engineer role before moving into more technical interviews.
Candidates work through SQL problems involving joins, count, group by, having, and window functions like ROW_NUMBER, RANK, and DENSE_RANK. The interview also includes behavioral questions about production experience and collaborating with cross-functional teams, plus a practical debugging task involving a DDL query.
This final round uses two datasets and asks the candidate to create graphs and walk through the analysis. It is less about pure coding and more about data interpretation, communication, and explaining the approach clearly.