Yes, that makes a lot of sense and is a good way to approach datasets IMO. Maybe you could just implement a simple command/recipe that each developer can run to add their annotations to the "master database". Under the hood, that would just use db.get_dataset (personal DB) and then db.add_examples (master DB). Since it's just a Python script, you can do pretty much anything in there – for example, you could even make it send you a Slack notification like "Arnault just added 345 examples to master dataset xyz and said: 'Annotation done! Let me know what you think!'" 
If you have overlapping annotations (e.g. same data annotated by different people, potentially with conflicts), you might also want to look at the review recipe as a way to create "merged master datasets": Built-in Recipes · Prodigy · An annotation tool for AI, Machine Learning & NLP
You might also find this comment helpful – it's more about general strategies for annotating and developing together as a team: