Target Data Engineer interview typically runs 7 rounds: recruiter screen, technical 1, technical 2, hiring manager, bar raiser, HR, and team case study. It usually takes several weeks and is highly structured with broad technical depth.
$120K
Avg. Base Comp
$147K
Avg. Total Comp
6
Typical Rounds
3-5 weeks
Process Length
We’ve seen Target evaluate data engineers with a surprisingly wide lens, and that breadth is the main thing candidates underestimate. In this experience, the questions moved well beyond core SQL into Python, PySpark, ML, and general data engineering concepts, which suggests Target is looking for someone who can operate across the stack rather than stay in one narrow lane. The recurring pattern is that they don’t just want correct answers — they want to see whether you can connect tools, tradeoffs, and system behavior in a practical retail context.
Another signal we’ve noticed is how heavily Target leans on resume-level specificity. Our candidates report being pushed to walk through prior projects in detail and explain exactly what they owned, which means vague summaries tend to fall flat. The case-style prompts also matter: they seem designed to test how you reason through ambiguity, not just whether you can solve a clean technical problem. That makes the interview feel less like a checklist and more like a working session.
One non-obvious risk here is misalignment between what you say you know and what they still probe. In this case, the candidate explicitly said they lacked NoSQL experience, yet still got deep NoSQL questions, and even saw some conceptual pushback on Kafka. That tells us Target may prioritize coverage over tailoring, so candidates should be ready for a broad, sometimes inconsistent technical conversation and stay calm when the interviewer presses on an area outside their day-to-day background.
Synthetized from 1 candidates reports by our editorial team.
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Real interview reports from people who went through the Target process.
The hardest part for me was that the technical rounds felt broader than I expected for a data engineer role. After the recruiter screen, I went into a series of interviews that covered SQL, Python, PySpark, ML, project deep dives, and a case study tied to the team. The first technical round was already pretty packed, and then the next one followed the same pattern but at a noticeably higher difficulty with a more senior interviewer. By the time I got to the hiring manager round, it had shifted more into open-ended scenario questions and another case study, which felt less like a coding interview and more like they were testing how I think through ambiguous problems. There was also a bar raiser and then HR at the end, so the process was long and pretty structured overall.
What stood out most was how much they cared about details from my resume and past work. I was asked to skim through previous projects and explain what I had done, and the technical questions were not limited to one area. I got SQL at a medium-to-advanced level, Python coding, and PySpark, and there were also questions that went into data engineering concepts more generally. One thing that frustrated me was that I clearly said I didn’t have NoSQL experience and had mostly worked with SQL databases, but I still got deep NoSQL questions anyway. Another odd moment was that the interviewer kept insisting Kafka was not a distributed log system, even after I tried to clarify it. The interviewer was otherwise kind and did try to help me through some answers, but the interview was very long and felt a bit inconsistent in places. I didn’t get an offer in the end, so my main takeaway is to be ready for a wide-ranging technical loop and not assume they’ll stay strictly within your stated experience.
Prep tip from this candidate
Be ready for medium-to-advanced SQL, Python, and PySpark questions, plus a resume deep dive and team-specific case study. Also prepare to explain data engineering fundamentals clearly, since Kafka and NoSQL came up even when they were outside the candidate’s direct experience.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Target
Write a query to identify customers who placed more than three transactions each in both 2019 and 2020
| Question | |
|---|---|
| Average Order Value | |
| Monthly Customer Report | |
| Over-Budget Projects | |
| Hurdles In Data Projects | |
| Black Friday Shopping Spree | |
| Common Prefix | |
| Client Solution Pushback | |
| Sales Leaderboard | |
| Your Strengths and Weaknesses | |
| Slow OLAP Aggregations | |
| 2nd Highest Salary | |
| Random SQL Sample | |
| Total Spent on Products | |
| Cumulative Sales Since Last Restocking | |
| Marketing Channel Metrics | |
| Post Composer Drop | |
| Random Forest Explanation | |
| Monthly Product Sales | |
| Max Quantity | |
| Total Transactions | |
| ATM Robbery | |
| Valid Anagram | |
| Find Mismatched Words | |
| String Palindromes | |
| Why Do You Want to Work With Us | |
| Azure Kubernetes Infrastructure | |
| Weighted Average Sales | |
| Generative AI Privacy | |
| Empty Neighborhoods |
Synthesized from candidate reports. Individual experiences may vary.
An initial conversation with recruiting to review your background, resume, and fit for the Data Engineer role. This stage appears to set expectations for a broad technical loop rather than a narrowly scoped data engineering interview.
The first technical round covers a wide range of topics, including medium-to-advanced SQL, Python coding, PySpark, and general data engineering concepts. Interviewers also dig into your resume and past projects, asking you to explain what you built and how you approached prior work.
A second technical round follows a similar structure but at a noticeably higher difficulty level and with a more senior interviewer. In addition to SQL, Python, and PySpark, candidates may also face deeper questions on areas like NoSQL and broader platform concepts.
This round shifts away from pure coding and into open-ended scenario questions and a team-specific case study. The focus is on how you think through ambiguous problems, make tradeoffs, and apply your experience to the team’s needs.
A separate interview with a bar raiser adds another evaluation of technical depth and overall hiring bar. Based on the experience shared, this appears to be part of a structured loop that tests consistency across interviewers.
The process ends with HR, likely covering final process checks, compensation or logistics, and next steps. After this stage, the team makes the final decision.