Tiger Analytics Data Analyst interview typically runs 4 rounds: online coding test, two technical interviews, HR round. It usually takes a few weeks and is notably SQL-heavy.
$96K
Avg. Base Comp
$132K
Avg. Total Comp
4-5
Typical Rounds
2-4 weeks
Process Length
Our candidates consistently found that Tiger Analytics cares less about polished buzzwords and more about whether you can reason through data systems in a practical way. We’ve seen multiple reports of the interview leaning heavily on SQL, but not in a surface-level way: candidates were pushed on database concepts, live record handling, and query logic that reflects real production thinking. One candidate who received an offer said the most revealing part was being asked how to maintain live records in a database, which is a strong signal that Tiger wants analysts who understand how data behaves over time, not just how to extract it.
A recurring theme is that the company also likes to mix in problem-solving under pressure, even for analyst roles. Our candidates report live coding-style questions alongside SQL, including algorithmic problems like Number of Islands and ranking employees by salary within departments. That combination suggests they’re screening for people who can move between analytical reasoning and technical execution without getting flustered. We also saw Power BI, data modeling, OOPs, and DBMS basics come up, which tells us the bar is broader than many candidates expect.
The non-obvious trap here is assuming this is a pure analytics or dashboard interview. It isn’t. Tiger Analytics seems to value candidates who can explain why a query or schema choice makes sense, especially when the question shifts from textbook SQL to scenario-based design. In our view, the strongest candidates are the ones who can stay grounded in fundamentals while showing they understand how those fundamentals apply in messy, real-world data environments.
Synthetized from 2 candidates reports by our editorial team.
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Real interview reports from people who went through the Tiger Analytics process.
The hardest part for me was realizing early that this interview was much more SQL-heavy than I expected. The process started with an online coding test that had three medium-level coding questions, and then I moved into two technical interviews followed by an HR round. In the technical rounds, they kept coming back to practical SQL and database concepts, along with OOPs and DBMS basics. I was also asked a live coding-style question around Number of Islands, so it wasn’t just pure SQL — they did want to see how I approached problem solving under time pressure.
What stood out most was how in-depth the SQL discussion got. It wasn’t just writing simple queries; they asked conceptual questions like how to maintain live records in a database, which felt more like testing whether I understood real-world data handling and not just syntax. The difficulty was mostly easy to medium overall, but the live coding and SQL problem-solving made it feel more demanding than a standard analyst interview. The HR round was straightforward and covered basic personal questions like family background and hobbies, so nothing unusual there. I ended up getting the offer, and my main takeaway was that anyone preparing for Tiger Analytics should spend extra time on SQL concepts and be comfortable explaining database design choices, not just solving algorithm questions.
Prep tip from this candidate
Focus heavily on practical SQL and database concepts, especially questions about maintaining live records and writing queries under pressure. Also be ready for a medium-level coding test and a live coding problem like Number of Islands, plus basic OOPs/DBMS questions in the technical rounds.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Tiger Analytics
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Closest SAT Scores | |
| Experiment Validity | |
| Prime to N | |
| Find the Missing Number | |
| Bagging vs Boosting | |
| Get Top N Frequent Words | |
| New Partner Card | |
| Minimum Absolute Distance | |
| Missing Housing Data | |
| Target Indices | |
| Median O(1) | |
| Assumptions of Linear Regression | |
| Digit Accumulator | |
| Matrix Rotation | |
| Count Transactions | |
| KNN From Scratch | |
| Possible Triangles | |
| Yelp-like System | |
| Data Preparation for Imbalanced Data | |
| Finding the Maximum Number in a List | |
| String Palindromes | |
| Minimum Directional Path | |
| Normal Distribution Sample | |
| k-Means from Scratch | |
| Area Under the ROC Curve | |
| Maximum Common Substring | |
| Employee Salaries | |
| Empty Neighborhoods | |
| Top Three Salaries |
Synthesized from candidate reports. Individual experiences may vary.
An initial screening based on your background and fit for the Data Analyst role. In one experience, this was described as a standard resume screen before moving into the rest of the process.
A conversational HR round covering basic personal questions such as family background, hobbies, and general fit. This stage was straightforward and served as an early filter before the technical interviews.
A timed assessment with three medium-level coding questions. Candidates reported that this test could include problem-solving beyond SQL, setting the tone for a process that is more technical than a typical analyst interview.
The first technical round focused heavily on SQL and database fundamentals, along with analytics basics. Questions included practical SQL queries, ranking logic, and conceptual database topics such as maintaining live records.
A second technical round that continued probing SQL depth, DBMS concepts, and OOPs basics. Candidates also saw live coding-style problem solving, such as a Number of Islands question, to assess how they think under pressure.