What does it take to break into quant?

This comes up most from people at less well-known schools, asking how to make the college tag stop mattering, and the honest answer is that it never stops mattering completely, but it matters in fewer places than you probably think. It matters at the door, because the resume screen is a black box: some firms cast a wide net and send online assessments to almost everyone, so you get a shot regardless of school, while others lean hard on pedigree to filter. In India this is sharper still, since tier 1 colleges get on-campus recruiting and everyone else works LinkedIn and cold outreach. And it matters again at the very end, in a step most people do not even know exists:

after your interviews, when you are in limbo between the final round and the offer, you can still get rejected even if you did amazingly. Passing or failing a round is binary, and if there is not enough room for everyone who passed, they do a stack rank and pick the strongest backgrounds, even over someone who interviewed better. That includes experience, and notably your school too.

— MyAngelKazusa

So "crush the interview and you are in" is not quite true, but what it still means is that you have to be clearly strong. In general, your school does not decide how you do, because the interview is mostly about competence and how fast you learn, tested across your problem-solving capabilities and domain depth for your role of interest, none of which cares where you studied. The move, then, is to be undeniable on skill. Competitive math/programming with a decently honest metric (rating, ranking) are good ways to practice your logic/problem solving, and can sometimes even be additional ways to get your foot through the door. For developer roles, get good at low-level C++, the modern, concurrency, and performance sides of it. Build something relevant like an order book or a matching engine, and put it on GitHub. For quant roles, split it by track: quant trading leans on fast mental math (drill arithmetic on zetamac), expected value and probability, and market-making games where an interviewer adversarially trades against you; quant research leans harder on statistics (regression, hypothesis testing, MLE), plus probability and often coding/ML for systematic desks. Many relevant problems, books, and other resources for these topics can all be found on this site.

The odds of getting unlucky at a high skill level are low, so it is not a reason to shirk on practice, because being strong is simply how you give yourself the best shot. If the effort of that deters you, consider pursuing a different sub-field.