The shift from relying on AI-assisted coding to facing senior-level interview loops is exposing a real gap in how many data professionals now work. This reader spent years at a FAANG company, moving fast with AI handling the heavy lifting, and now finds that the skills that got them promoted are not the ones being tested. The stats intuition and case study chops are solid, but the coding muscle has atrophied. That is not a personal failing; it is the natural consequence of a workflow built around speed and iteration. But it also means the preparation strategy has to change, and fast.
The practical question is not whether LeetCode mediums are required, but what level of polish the interviewers actually expect. For a senior role, the bar is not about acing a hundred algorithmic puzzles. It is about demonstrating that you can design a solution, communicate the logic clearly, and handle the messy parts of data manipulation without freezing. Pandas and SQL are likely to be the bread and butter for the data manipulation rounds, but relying on them alone is a risk. Interviewers want to see that you can think through a problem from start to finish, even if the final code is not perfect. Describing the approach in pseudocode is a good start, but it is not enough if you cannot translate that logic into working code when pressed. The expectation is not perfection; it is fluency.
What this reader is really facing is a common trap of the AI era: the tools that made them productive in their last role have not prepared them for the way hiring still works. The interviews are not a reflection of the job, but a proxy for how you think under pressure. The good news is that this is trainable. Focus on the highest-yield areas: practice medium-level LeetCode problems that involve arrays, strings, and hashmaps, and get comfortable with pandas operations that mirror real-world data cleaning and aggregation. Time-box your practice, simulate the pressure, and force yourself to narrate your thought process out loud. That is the skill that matters most, not memorizing solutions.
The takeaway is straightforward: do not assume that your day-to-day work has prepared you for the interview gauntlet. It has not, and that is okay. Build a deliberate prep plan that covers both algorithmic thinking and practical data manipulation. You do not need to become a competitive programmer, but you do need to prove that you can go from a blank screen to a working solution without AI holding your hand. That is the bar for senior, and it is one you can clear with focused effort.