Agreed, but would go even further and say it has to be very small and early. Once a workflow/architecture has been established the most complex decisions have already been made. That zero-to-one architecture phase of taking a prototype to production is black magic.
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Yeah ideally you're helping launch the MVP. Even if you don't get in that early enough to write the webpack config, your job still probably involves building new features, wiring up the UI, adding new tables and figuring out what to to index, design parts of an internal API, etc
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Ah yes, contributing to shipping the MVP is exactly it. Enough ambiguity where the best engineering decision isn't obvious.
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If you find yourself hating other people's decisions then you joined too late. Aim to join early enough that everyone else hates YOUR decisions.
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Aha yes! Always fun getting git blamed and the "why did you do it like this? It's not very good at handling my business logic" message. And of course I can't even remember writing the code.
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The best is when you’re annoyed w code, git blame, and it’s you
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Interesting, though not so sure about the combination of fullstack dev & data science. How many people have you seen who are good at both?
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Yeah fair point. That intersection is more rare. The point is more so that you at least learn how to gather data, figure out what parts you need to make decisions, work with it until you can act on a result, at least know how to apply basic stats and visualize things.
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What’s your range for small vs mid? By # of engineers
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