Code-switching has seen growing interest in recent years as an important multilingual NLP phenomenon.Generating code-switched text for data augmentation has been sufficiently well-explored.However, there is no prior work on generating code-switched text with fine-grained control on the degree of codeswitching and the lexical choices used to convey formality.We present COCOA, an encoder-decoder translation model that converts monolingual Hindi text to Hindi-English code-switched text with both encoder-side and decoder-side interventions to achieve finegrained controllable generation.COCOA can be invoked at test-time to synthesize codeswitched text that is simultaneously faithful to syntactic and lexical attributes relevant to code-switching.COCOA outputs were subjected to rigorous subjective and objective evaluations.Human evaluations establish that our outputs are of superior quality while being faithful to desired attributes.We show significantly improved BLEU scores when compared with human-generated code-switched references.Compared to competitive baselines, we show 10% reduction in perplexity on a language modeling task and also demonstrate clear improvements on a downstream code-switched sentiment analysis task.