The majority of CS&E codes today are written in C++, and many are now based on C++11 or C++14. If you know C++11 well, you're probably on the safe side. C and C++ are the languages of choice for the many libraries on which other codes then build. This is true across the spectrum, for everything that involves linear algebra, optimization, finite elements and finite volumes, and support libraries such as for parallel and task-based programming. You have to be proficient in C++ today to be a productive computational scientist.
Fortran is used in a large number of legacy code bases. These codes are, for the most part, stand-alone rather than building on other libraries. Many of these code bases date back decades and have hundreds of thousands or millions of lines of code. They are very difficult to replace by newer codes, and consequently continue to be maintained. Many of them have over the years been converted to Fortran-90, and maybe Fortran-2008, but few use modern language features extensively. Most younger folks who end up using these codes learn Fortran as they go, rather than coming extensively prepared with long Fortran experience.
Many of us use Python for small-scale data processing and plotting. There are, however, a growing list of libraries that are either exclusively built on Python, or have Python bindings. That is true to a degree in the finite element community. Many of the linear algebra packages have sufficiently small interfaces that they are nearly completely wrapped with Python interfaces.
No other language plays a major role in Computational Science and Engineering, although there are of course proponents for this or that language and use case. That said, if you move into the data sciences, R is quite important.