I was a quantitative developer working alongside a quantitative trader in a small 'proprietary trading fund'. This is roughly how all quant funds are separated. There is the "technology" side, which builds the data/trading infrastructure and then there is the "research" side that generates the trading strategies to run on the infrastructure.

My job involved anything from hooking up to brokerage APIs to optimising MySQL replication topologies. Quite varied!

In a way, it wasn't too different from your average startup, with the possible exception that you deal with a non-trivial amount of data from day #1 (hundreds of millions of rows are not uncommon).

Open source has gained significant ground in funds these days. Python/R are now the "default" go to languages for quant trading research, with some MatLab too. Libraries such as NumPy, SciPy and pandas have really brought 'algo trading' to the 'retail' (algo) sector as well.

.NET is still generally used quite a lot in investment banking, particularly C# for front-office GUI code, and C++ for any legacy number crunching libraries.