Tiger Analytics Data Analyst candidates reported SQL-heavy technical discussions, data-modeling concepts, and, in one report, an online coding test and live coding.
$108K
Avg. Base Comp
$132K
Avg. Total Comp
3-4 rounds
Typical Rounds
2-4 weeks
Process Length
Tiger Analytics Data Analyst interviews reported here put practical SQL at the center of technical preparation. One candidate was asked to rank employee salaries within departments, then discussed Power BI, data-modeling concepts, and more advanced query building. That report says the conversation began with fundamentals before moving into less predictable, scenario-based analytics questions, so prepare to explain your reasoning rather than only produce a query.
A second candidate described an online coding test with three medium-level questions, two technical interviews, and an HR conversation. Their technical interviews returned repeatedly to SQL, database concepts, OOPs, and DBMS basics, including how to maintain live records in a database. They also encountered a live coding problem based on Number of Islands. For this role, practice writing SQL under time pressure, articulating database-design tradeoffs, and solving a coding problem aloud.
The reports are limited to two candidates, so the coding-test sequence may not apply to every applicant. Still, both accounts point to SQL and data concepts as the clearest preparation priorities. Review window functions for within-group rankings, be ready to discuss data models and database behavior clearly, and use scenario practice to connect an analytical choice to a business situation. HR was described as straightforward in both accounts, following resume or personal-background discussion.
Synthesized from 3 candidate reports by our editorial team.
Had an interview recently?
Share your experience. Unlock the full guide.
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.
Share your own interview experience to unlock all reports, or subscribe for full access.
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 | |
| Top Three Salaries | |
| Experiment Validity | |
| Prime to N | |
| Top 3 Users | |
| Find the Missing Number | |
| Maximum Profit | |
| Bagging vs Boosting | |
| Get Top N Frequent Words | |
| New Partner Card | |
| Minimum Absolute Distance | |
| Retailer Data Warehouse | |
| Missing Housing Data | |
| Target Indices | |
| Production Model Monitoring | |
| Median O(1) | |
| Assumptions of Linear Regression | |
| Digit Accumulator | |
| Count Transactions | |
| Matrix Rotation | |
| KNN From Scratch | |
| Possible Triangles | |
| Yelp-like System | |
| Data Preparation for Imbalanced Data | |
| Finding the Maximum Number in a List | |
| String Palindromes | |
| Deciding Between Solutions | |
| Minimum Directional Path | |
| Normal Distribution Sample |
Synthesized from candidate reports. Individual experiences may vary.
One candidate described a usual resume screen followed by an HR conversation before the technical round. Another reported an HR round after technical interviews, so the placement of this conversation may vary.
One candidate completed an online test with three medium-level coding questions before technical interviews. This stage is reported once and may not be part of every Data Analyst process.
Candidates report SQL-focused technical questioning. Topics included ranking salaries within departments, practical queries, database concepts, data modeling, Power BI, OOPs, and DBMS basics.
One candidate reported a live Number of Islands problem, while another described a shift from fundamentals into scenario-based analytics questions. Expectation and format may vary by interviewer.
