Trend Micro Data Engineer interview typically runs 6 rounds: recruiter screening, online SQL assessment, technical virtual interview, in-person KQL whiteboard, alignment/culture fit, and HR behavioral. It took about 2-4 weeks and notably included a surprise KQL whiteboard round.
$113K
Avg. Base Comp
$154K
Avg. Total Comp
6
Typical Rounds
3-5 weeks
Process Length
Our candidates report that Trend Micro cares less about flashy architecture talk and more about whether you can operate comfortably in an Azure-centered data stack. The strongest signal in this experience was the repeated emphasis on Azure and Databricks, plus a domain-ownership mindset: the conversation shifted from “can you build pipelines?” to “can you own a data area and make decisions like a partner to the business.” That tells us the team is screening for engineers who can connect technical choices to a specific operational domain, not just execute tickets.
A recurring theme is the company’s preference for practical query fluency under real constraints. The SQL assessment leaned on grouping, filtering, and reconstructing messy data, but the surprise came later with KQL on a whiteboard. That matters: multiple candidates should expect the bar to include language adaptability, especially if the environment is Azure-native. We’ve seen that the questions themselves are manageable, but the interview becomes much harder when you have to express logic in a tool you haven’t used recently.
What makes or breaks candidates here is usually not raw difficulty, but whether they can stay precise when the stack changes. The candidate who shared this experience had strong senior-level background and still called out the KQL round as the biggest surprise. That’s a useful clue: Trend Micro seems to reward depth, but it also quietly tests whether you can ramp quickly into their ecosystem and communicate cleanly about tradeoffs, ownership, and data reliability.
Synthetized from 1 candidates reports by our editorial team.
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Real interview reports from people who went through the Trend Micro process.
Location: Taipei, Taiwan
I applied online through LinkedIn for a Senior Data Engineer role on the Business Intelligence Foundation team. My background is in mechanical engineering originally, but I'd been working in data engineering for several years across tech, FinTech, and MarTech companies, including a Senior Data Architect role at Agoda in Bangkok. After coming back to Taiwan, I spent about five months reviewing fundamentals before starting this job search.
Round 1: Recruiter Screening Standard first call with the recruiter to establish fit.
Round 2: Online SQL Assessment After the recruiter screen, I received a credibility assessment with two SQL questions. I'd say they were medium to hard difficulty.
The first question involved a table of records where consumers had read restaurant recommendations (and other recommendations). The task was to find all strictly recommended restaurants. The main idea was using GROUP BY and HAVING to filter based on the requirements.
The second question was about a recipe menu with pages numbered from 0 to the last page, but with some pages missing (for example, pages 1, 2, 5, 6 existed but pages 3 and 4 were missing). The task was to transform the page list into a pairwise left-page/right-page format, where page 0 is the left page, page 1 is the right page, page 2 is the next left page, and so on. The core challenge was finding all the missing pages first. I used generate_series to get the row numbers from 0 to the last page and worked from there.
Round 3: Technical Interview (Virtual) — Past Experience and System Design This was with the team lead and a staff engineer. They focused on my past experiences, how I handled large-scale data at Agoda, and system design.
Round 4: Technical Interview (In-Person, Whiteboard) — KQL This round was in person, and the two questions were in KQL (Kusto Query Language), not SQL. KQL is a SQL-like language but specific to Azure cloud. I only had two days to learn it before this interview (I had another interview at a different company at the same time, so I couldn't dedicate more time to it).
The first KQL question involved a user request log. The task was to filter out error requests (e.g., HTTP 500 errors) and find the most frequent requests from a specific region. In SQL you'd use SELECT, WHERE, and GROUP BY, but in KQL each line is its own query statement, which is a totally different structure.
The second KQL question required using JSON parsing syntax to parse user request data and calculate total counts and the average of click events. The questions themselves weren't super difficult conceptually, but writing them in a brand new query language on a whiteboard made them challenging.
Round 5: Alignment / Culture Fit Interview The team was happy with my skills and experience, so this round was more about getting aligned on expectations from both sides. We talked about Azure and Databricks technologies, and they mentioned I'd have ownership of a specific data domain.
Round 6: HR Behavioral Interview Typical behavioral questions: why did you leave your previous company, salary expectations, and so on.
The KQL whiteboard round was the biggest surprise. I'd never used it before and only had two days to learn it. If you're interviewing for a data engineering role at a company that runs on Azure, it's worth at least familiarizing yourself with KQL basics before your interviews, even if it's not listed in the job description.
Prep tip from this candidate
The online SQL assessment included a missing-page gap-finding problem that required generate_series, so make sure you're comfortable with sequence generation and pairwise transformations, not just standard aggregations. Also, Trend Micro's in-person technical round used KQL (Kusto Query Language for Azure) on a whiteboard, which candidates are rarely prepared for, so if you know the role involves Azure, spend at least a few days on KQL syntax before your interview.
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Topics based on recent interview experiences.
Featured question at Trend Micro
Write a query to repeat each int_numbers value by its own value in the output
| Question | |
|---|---|
| Your Strengths and Weaknesses | |
| 2nd Highest Salary | |
| Merge Sorted Lists | |
| Prime to N | |
| Largest Salary by Department | |
| Hurdles In Data Projects | |
| Cyclic Detection | |
| Groups of Anagrams | |
| Skyscanner Partner ETL | |
| Marketing Channel Metrics | |
| Sort Strings | |
| P-value to a Layman | |
| Flatten N-Dimensional Array to 1D Array | |
| Valid Anagram | |
| Complete Addresses | |
| Find Duplicate Numbers in a List | |
| Centralized Event Ingestion | |
| Target Indices | |
| Common Prefix | |
| Swiping App Design | |
| Portfolio Platform Architecture | |
| Get Top N Frequent Words | |
| String Subsequence | |
| Count Transactions | |
| Longest Increasing Subsequence | |
| Binary Tree Conversion | |
| Three Indexes Adding Zero | |
| Legacy System Heartbeat Monitor | |
| Ride-Sharing App Schema |
Synthesized from candidate reports. Individual experiences may vary.
A standard first call with the recruiter to confirm basic fit, background, and interest in the Senior Data Engineer role. This stage also appears to set expectations for the rest of the process.
Candidates complete a credibility assessment with two medium-to-hard SQL questions. The problems in this interview included identifying strictly recommended restaurants using GROUP BY/HAVING and reshaping a page list with missing values into a left-page/right-page format.
A virtual technical interview with the team lead and a staff engineer focused on prior data engineering experience, especially handling large-scale data, and on system design. The discussion was tied to the candidate's experience at Agoda and similar production data work.
An onsite whiteboard round tested Kusto Query Language (KQL) rather than SQL. Questions included filtering error requests and finding the most frequent requests by region, as well as parsing JSON request data to compute counts and averages.
This round was more about mutual alignment on expectations, ownership, and team fit after the technical rounds. The conversation covered Azure and Databricks technologies and the data domain the candidate would own.
A final behavioral conversation covering standard topics such as reasons for leaving a previous company and salary expectations. This appears to be the last step before the offer decision.