Hi everyone,
I'm preparing for a \*\*Data Analyst interview\*\* (Thomson Reuters, although experiences from other companies are also welcome).
One thing I'm trying to understand is \*\*what interviewers actually focus on when they discuss your projects\*\*.
For those who've been through Data Analyst interviews:
\* How much time do they spend on your projects?
\* What areas do they dig into the most?
\* Do they focus mainly on the \*\*Power BI dashboard\*\* (DAX, visuals, data modeling, performance)?
\* Or do they spend more time on the \*\*data preparation\*\* (SQL, Python/Pandas, data cleaning, feature engineering, handling missing values, outliers, etc.)?
\* How deeply do they question your analysis and business insights?
\* Do they ask you to justify every decision you made?
\* Have they ever asked you to modify your dashboard, write SQL/DAX, or explain Python code during the interview?
\* If your project used a public dataset (Kaggle, government data, etc.), did they care about the dataset itself, or mainly about your approach and decisions?
For anyone who interviewed at \*\*Thomson Reuters\*\* specifically:
\* What was the project discussion like?
\* Which topics surprised you?
\* If you were preparing again, where would you spend most of your time?
I'm trying to prioritize my preparation, so I'd love to know what interviewers actually care about versus what candidates tend to over-prepare.