Samsung Electronics Data Engineer interview typically runs 5 rounds: technical assessment, technical interview, English conversation, team interview, manager interview. The process takes about 2 weeks and emphasizes fundamentals and detailed feedback.
$135K
Avg. Base Comp
$147K
Avg. Total Comp
5-6
Typical Rounds
2 weeks
Process Length
Our candidates report that Samsung’s data engineering interviews are less about flashy architecture and more about whether you can reason cleanly through real data work. In one experience, the most relevant signal came from a hands-on assessment that mixed data extraction with analysis, which suggests they care about whether you can move from raw data to a usable answer without losing rigor. We also see a recurring emphasis on explaining your decisions clearly: the early conversations leaned on past projects, how specific data situations were handled, and even simple brainteasers that exposed how someone thinks under pressure.
A second pattern is how much weight they place on fundamentals. Multiple candidates described questions on basic programming and OOP concepts, including polymorphism, alongside practical prompts that looked simple but were meant to test composure and correctness. That tells us Samsung is screening for engineers who are solid on the basics and can stay precise when the question is intentionally understated. The strongest candidates in this process seem to be the ones who can connect their background to the work Samsung actually does, not just list tools they’ve used.
We’ve also noticed that the fit conversation matters more than it might at a pure software company. The interviews reportedly included casual English discussion and manager-level questions about whether the candidate understood the team’s day-to-day work. That combination points to a company that values practical alignment with the business as much as technical fluency. In other words, they want someone who can operate comfortably in a cross-functional, product-adjacent environment and communicate tradeoffs without overcomplicating them.
Synthetized from 1 candidates reports by our editorial team.
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Real interview reports from people who went through the Samsung Electronics process.
Cormac was really friendly and made the whole process feel comfortable, even though the interview itself was pretty challenging. I went through two interviews plus a technical assessment, and the assessment combined data extraction and data analysis, which was the part that felt most relevant to the role. The first conversation was mostly about my past experience, how I’d handled certain data situations, and a few brainteasers. It was less about coding depth and more about how I think through problems and explain my decisions. I also had a more general technical discussion where they asked me to walk through my background and responsibilities, whether I used any data engineering tools, and some basic programming and OOP questions like polymorphism and object-oriented concepts. One question that stood out was how to merge two numbers in Python, which felt simple on the surface but was clearly meant to see how I approach coding basics under pressure.
What surprised me was how much the interview leaned on fundamentals rather than advanced system design. There was also a casual English conversation round, and then a team interview and a manager interview focused more on fit and on whether I understood the work they actually do there. The process moved fairly quickly, around two weeks from application to interview. I didn’t get an offer, but the feedback I received was unusually detailed and genuinely helpful, with specific bullet points on how to improve and get more interviews in the future. If you’re preparing, I’d focus on being able to explain your past projects clearly, review OOP basics, and be ready for a hands-on assessment that mixes extraction and analysis rather than pure algorithm questions.
Prep tip from this candidate
Be ready to explain your past data work clearly and answer basic OOP questions like polymorphism, since those came up directly. Also practice a small Python coding task and a data extraction/analysis exercise, because the technical assessment was centered on those skills.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Samsung Electronics
Detect a cycle in a singly linked list.
| Question | |
|---|---|
| Categorize Sales | |
| Bias vs. Variance Tradeoff | |
| Valid Anagram | |
| Data Preparation for Imbalanced Data | |
| String Palindromes | |
| Target Value Search | |
| Impossibly Iterative Fibonacci | |
| Shortest Path Algorithms | |
| Your Strengths and Weaknesses | |
| LRU Cache 1 | |
| Gradient Descent Calculation | |
| Slow OLAP Aggregations | |
| 2nd Highest Salary | |
| Find the Missing Number | |
| Random SQL Sample | |
| Prime to N | |
| Paired Products | |
| Upsell Transactions | |
| One Element Removed | |
| Retailer Data Warehouse | |
| Cumulative Sales Since Last Restocking | |
| Completed Shipments | |
| Random Forest Explanation | |
| Detecting ECG Tachycardia Runs | |
| The Brackets Problem | |
| Hurdles In Data Projects | |
| Google Maps Improvement | |
| Missing Housing Data | |
| Groups of Anagrams |
Synthesized from candidate reports. Individual experiences may vary.
The process appears to start with an introductory conversation focused on your background, past data experience, and how you handle data-related situations. This stage also included a few brainteasers and was more about communication and problem-solving approach than deep coding.
Candidates then go through a more general technical interview covering data engineering tools, responsibilities from prior roles, and basic programming concepts. Questions included OOP fundamentals like polymorphism and simple coding prompts such as merging two numbers in Python.
A hands-on assessment combined data extraction and data analysis, which was described as the most relevant part of the process for the role. The emphasis was on practical data work rather than advanced system design or algorithm-heavy coding.
There was a casual English conversation round as part of the process. This appears to have been a lighter communication-focused stage rather than a technical evaluation.
The team interview focused on fit and on whether the candidate understood the actual work done at Samsung. It likely assessed collaboration, role alignment, and practical understanding of the team’s responsibilities.
The final interview was with a manager and also centered on fit and role understanding. This stage likely served as the final decision point before an offer or rejection.