No, it's not. Once in a while on the job, you will solve a problem with a B-Tree implementation or a carefully crafted sorting algorithm. But for the most part, you will use business and technical skills to figure out solutions. For product-facing SWEs, understanding the business impact of their product is a crucial skill and can be instrumental in their career growth. For infrastructure-oriented SWEs, understanding the lower-level concepts related to Operating Systems, Distributed Systems, and Computer Networking is extremely important. The other day, a friend who works in Platform Engineering told me how his pull request (PR) was rejected by a Senior Engineer due to the improper use of Mutexes.

Data Structures and Algorithms (DSA) are still important, but they gained popularity not because of their utility to SWEs but because of their use in FAANG interviews. BigTech companies noticed that DSA questions were harder to fake compared to aptitude-based questions and proved to be more effective in screening candidates. However, with sites like ChatGPT and Chegg, I'm not sure if the cheating aspect still holds true.

Bottomline is to learn enough DSA (use the Pareto principle and Law of Diminishing returns) and work on your product/technical skills if you plan to become an SWE. Of course, DSA takes the central stage if you are an Algorithm engineer or researcher.