Two candidates reported Target Data Engineer interviews that ended without offers. Questions centered on SQL, Python, PySpark and Spark architecture, with project deep dives, case studies and manager conversations.
$123K
Avg. Base Comp
$150K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Two candidate reports describe the Target Data Engineer interview, and both ended without an offer. The loops differed in length, but they agree on one thing: the technical questions go deep and fundamentals get tested.
One candidate described a long, structured loop: a recruiter screen, technical interviews, a hiring manager round, a bar raiser and an HR conversation. The technical rounds covered SQL at a medium-to-advanced level, Python coding and PySpark, plus ML questions, project walkthroughs and a team-specific case study. The second technical round was harder and run by a more senior interviewer. The hiring manager round moved toward open-ended scenarios and another case study. This candidate also got deep NoSQL questions after saying they had mostly worked with SQL databases, and described a disagreement with an interviewer over whether Kafka is a distributed log system.
The other candidate, who was referred, had a recruiter phone screen and two virtual rounds with potential managers. The questions pressed on Spark architecture, especially wide versus narrow transformations, along with SQL and project discussion. They felt their distributed computing foundations were not strong enough.
To prepare, be able to explain Spark internals in your own words, write SQL and PySpark comfortably, and walk through past projects in detail. If a tool is outside your experience, say so plainly and reason from related concepts. With only two reports, treat the stage list as a likely pattern rather than a fixed format.
Synthesized from 3 candidate reports by our editorial team.
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Real interview reports from people who went through the Target process.
I went through multiple rounds of phone interviews and video calls with Target for a Data Engineer role. The process involved talking with managers across different levels, from store to regional leadership, which gave me a sense of how the organization is structured. The interviews were behavioral and situational in nature rather than heavy on technical depth. Nothing was algorithmically brutal, but the questions definitely required thoughtful, structured responses. I remember being asked about a time I handled a difficult situation, which was less about a specific technical solution and more about how I communicated and problem-solved under pressure. One thing I noticed was that the interviewers seemed genuinely interested in my background and past experience — they dug into my work history in depth, which meant having concrete examples ready was essential. The whole process moved pretty quickly, and honestly, it felt more like a conversation than a formal interrogation. I got the sense that they were trying to get to know me as much as assess my technical chops. By the end, I received an offer.
Prep tip from this candidate
Prepare detailed STAR-format stories from your past work, especially around handling difficult situations and cross-team collaboration — Target's interview process is heavily behavioral and focuses on your actual experience rather than algorithmic problem-solving. Have concrete examples ready for every role on your resume, since interviewers at multiple levels will ask about your background in depth.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Target
Select the 2nd highest salary in the engineering department
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Synthesized from candidate reports. Individual experiences may vary.
Both candidates describe starting with a recruiter conversation. One, who was referred, called it a phone screen to coordinate scheduling and discuss their background. Be ready to summarize your data engineering experience and the tools you have used.
One candidate reports a first technical round covering SQL, Python, PySpark and ML, then a harder second round with a more senior interviewer. Another reports pointed Spark architecture questions, including wide versus narrow transformations. Expect project deep dives, and be ready to say what you have not used.
The referred candidate had two virtual rounds with potential managers, covering Spark, SQL and past projects. The other candidate described a hiring manager round of open-ended scenario questions and another case study. Practice reasoning aloud through ambiguous problems.
One candidate reports a bar raiser round followed by an HR conversation at the end of a long loop. The other candidate did not describe these stages, so they may not apply to every team or level. Ask your recruiter what the final stages will cover.
