You have received two types of answer so far:

1. You don't, and

2. Here are some practises used by effective engineers.

I think (hope?) the true answer to your question lies somewhere between the two answers you already have.

I hope it can be learned (it's the thing I'm gunning for, anyway) but I don't think effective engineering practises alone will get me there. They can make me a great engineer, for sure, but at this point we're talking beyond great.

I think (again, hope, anyway) that the key is learning things from outside your discipline, and integrating those things into a general framework that informs your decision making and design in your profession.

There are a lot of smart people writing books where they share their insights in their own niche (examples of niches include manufacturing, financial risk management, gambling, product development, network engineering, behavioural economics, negotiation, applied statistics, psychology, system safety, forecasting, and so on). These works tend to be about very specific things, but after a while you start to see underlying concepts that pop up time and time again and support the most important principles in each field.

It might sound like those things have nothing to do with engineering (and I'm sure a lot of people would hope for more technical subjects) but from my experience, the actual coding bit is surprisingly small compared to the one where you would like omniscience but will have to settle for a proxy.

Sometimes these principles are stated outright, other times it's not so obvious when they apply. For example, one recurring idea is hypothesis testing: view the world not through the lens of your knowledge, but through a set of hypotheses you need to test. I'm currently validating the more general applicability of this idea by, among other things, trying to think of poker bets as hypotheses -- did the other players respond as I had expected? If not, what consequences does that have for my idea of the table? -- similar to how traders on Wall Street might pull their trades if they lose faith in their backing hypothesis, and a project should be pulled if the MVP falsified the hypothesis about the market.

However, there are at least five problems with this method of mine.

1. Critically, I have no idea if it works. Only my perception of it working.

2. Research indicates human learning is incredibly contextual and there might not be such a thing as meaningful cross-disciplinary learning at all. This indeed seems like a fatal problem I have yet to address. (In the mean time, I'll keep doing what I'm doing because of the implied odds: if the research doesn't apply to what I'm doing (small chance) the compounding from having started early and not pausing makes it worth it. Also, it's fun to learn things.)

3. It takes a lot of time. I spend many hours a day reading, and then even more finding ways to put my learning into practise.

4. It relies on being able to sniff out good books. These are not necessarily the popular books, but ones that contain plenty of information that generalises. Sometimes these works even contradict general wisdom or consensus, which is particularly dangerous. It's easy to become a crackpot. Key is probably to give priority to scientific rigour -- or even better, a proven track record (beyond statistically plausible fluke.)

5. Once armed with the right reading, one needs also to be good at generalizing from specific ideas across disciplines to a more broadly applicable framework. I don't think this is a special skill, but again, I don't know.