For the the type of role I described in my post, being able effectively define metrics to quantify a business problem (e.g. how can we better engage with group X on our platform) and work with product engineering to build those features (including helping to define the data collected and how it's stored) into the platform is more important than tweaking a bunch of parameters in a machine learning model.

A lot of those folks are not only thinking of ways to quantify a business problem, they are actually thinking of new business problems (e.g. what does it even mean to "engage" on our platform). It can be quite creative and challenging.

Unless you are writing the core algorithms or working as a statistician, a lot of ML jobs are some variation of the above - basically coming up with ways to turn business problems into data/features you can feed into a model and picking an appropriate model. How much code you write and the tools/languages you are using will depend on the job and size of the company.