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.
$90K
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.
Synthesized from 1 candidate report by our editorial team.
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Real interview reports from people who went through the Eab process.
Overall, I had a positive but fairly average interview experience with EAB for the Data Engineer role. I applied online and first had an aptitude test that was mostly basic pattern recognition and math, which felt like a quick filter rather than a deep technical screen. After that, I moved into a phone screening that was mostly resume-based, along with SQL basics like joins and unions. That round was straightforward and conversational, and it seemed like they wanted to confirm my background fit the role before going deeper.
The later rounds were more hands-on. I had a live coding and behavioral interview where I was asked about working in production, collaborating with cross-functional teams, and solving SQL problems involving joins, count, group by, and having. One question that stood out was explaining ROW_NUMBER, RANK, and DENSE_RANK, so it helped to be very clear on how those window functions differ. I also had to debug and correct a DDL query to insert values into rows, which was a little more practical than I expected. The final data analytics round used two datasets and asked me to create graphs and talk through the analysis, so it was less about pure coding and more about how I approached the data and communicated my thinking. Overall, the process felt structured and reasonable, but there was a decent amount of SQL and applied data work. I didn’t get an offer, so my main takeaway is to be ready for both SQL fundamentals and a few practical debugging/analysis tasks, not just algorithm-style questions.
Prep tip from this candidate
Be ready to explain ROW_NUMBER vs RANK vs DENSE_RANK clearly, and practice SQL joins, unions, count/group by/having, plus debugging a DDL statement. It would also help to rehearse talking through a small analysis from two datasets and describing the graphs you’d build.
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Topics based on recent interview experiences.
Featured question at Eab
Write a SQL query to count transactions filtered by several criterias.
| Question | |
|---|---|
| Why Do You Want to Work With Us | |
| Merge Sorted Lists | |
| Prime to N | |
| Largest Salary by Department | |
| Find the Missing Number | |
| Top 5 Turnover Risk | |
| The Brackets Problem | |
| Flight Routes | |
| Cyclic Detection | |
| String Mapping | |
| Missing Housing Data | |
| Flatten JSON | |
| Valid Anagram | |
| Find Duplicate Numbers in a List | |
| Hurdles In Data Projects | |
| Target Indices | |
| Swap Variables | |
| Transformer Encoder Layer | |
| P-value to a Layman | |
| New Resumes | |
| Move Zeros Back | |
| Total Transactions | |
| Binary Tree Conversion | |
| Slow SQL Query | |
| Targeted sum | |
| String Palindromes | |
| Equal Binary Subarrays | |
| Double Card Value | |
| Find Square Root |
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.
