What distinguishes great software engineers? (2019) [pdf]

I think passion is everything. Everyone is 10x at their passion. Strive to have passion in all that you do.

It’s interesting there are so many different answers given to this question whenever it is asked. If we asked, “What makes a good surgeon?” or as pointed out in another comment, “What makes for a great basketball player?” it seems that there would be less debate.

It seems to me that this debate is related to another question, “Why do so many software projects fail when you don’t see any skyscrapers collapsing under their own weight?”

If you could build a skyscraper according to rules limited only by our imagination, then perhaps we’d see a lot more of them collapsing. And given that frequency of failure, we’d see a lot more debate about what makes for excellence in skyscraper-building.

Maybe a better question to ask is, “What makes for a poor software engineer?” That question seems a lot easier to answer. A poor engineer takes a long time to write unreadable, unmaintainable, buggy code that only partially solves problems that no one has. Ironically, such code often goes unnoticed by its very nature: it takes a lengthy period of time to even come into existence, when it does exist it is so buggy that its failure seems related to bugs rather than that it solves no problems, and the unreadable nature of the code is mistakenly attributed to essential complexity rather than incidental.

And that last point is where I think we get closest to an answer: Good engineers reduce complexity. Bad engineers magnify complexity.

Tolstoy wrote, “All Happy families resemble one another, but each unhappy family is unhappy in its own way.” It seems to me that the same could be said of software engineers.

In grad school we had a forced module on information systems in HR in which one case was about RBC and their new formerly-Bain Director of HR Data and the 3 or 4 year effort to “quantify” labor. Cases should be taken with some skepticism but let’s assume the case was correct.

I wanted to tear my hair out. Not only was the output or ultimate deliverable of this gargantuan activity an infographic of largely non falsifiable random words (trustworthy, reliable, predictable, etc etc), I did a quick back of the envelope that the approach to repeatedly solicit feedback on every employee from every business line and every customer they intersect could generate 5 trillion outbound survey “friction” onto their customers and stakeholders in a given year give or take 2 orders of magnitude. What does that cost in terms of non-enjoyment? Does it even work, and at what response rate at that scale? And what does it cost to maintain, since surely the Canadian treasury at least sort of requires or at worst encourages banks to employ trustworthy employees. Worse, how implementable are the outcome and/insights? For a real open requisition is evaluating “trustworthiness” at time of hire possible? Is it possible to compare two candidates on a relative or absolute basis on this metric? And if they are hired, going forward how is that tracked? More surveys?

Perhaps the case wasn’t as bad, and I’m sure the OP HB post of this paper has subtleties in survey design or methods I don’t appreciate, but just to pick on them by cherry picking the first few attributes that stick out: passionate, systemic, data driven, focused, hardworking, persevering, etc?!?!?

Worse, where’s the actual data? Like when Google apparently decided to get rid of middle management it felt like a reversible scientific experiment with measurable outcomes and metrics. This doesn’t. For example, from personal experience, in investment banking being hard working especially at the very lowest and newest ranks is similarly lauded and required and emphasized. But with a fairly simple app that tracks second level activity of which app is open and when and can assign a rule a rule based score, when reviewing a few years of data, the data contradicted the folsky saying.

The data showed above XX hours per week for me was non-restorative, and further above XX hours would create some incremental inefficiency increase, and above even that was a “tech debt” like factor which later required recuperation (which could also be calculated but maybe that was an accident). In English: if (simple numbers) working 10 hours leads to 8 hours of work and 2 hours of goof off for some productivity, maybe working 14 hours is 10/4, and maybe working 17 is 11/6 and also requires 2 hours of rest at a subsequent trailing period. Depending on what’s going on, maybe it makes sense to do that, maybe it doesn’t. The software engineer view would be much more interesting.

It would be fascinating to compare even arbitrary metrics of consenting individuals across a large pool. Who gets the biggest bonus relative to hours worked? What time do peak performers come in and leave at and are there any wfh days? Even trivial but theoretically transferable insights would help: “what percent of top performers drink no caffeine or tea” or “pair programming and velocity”. Heck, buzzfeed baiting analysis like “do top engineers type faster? A 10 part slideshow on why you should switch to a mechanical Dvorak keyboard” or “Can you type XYZ words per minute? Find out if you are dragging your team down”.

Anything but more lengthy repetitions of “trust” and “respect” and whatever the word of the day is.

Basketball has almost an endless list of examples of superior talent not excelling compared to those with less talent (this is objectively measured physically, where a player with clear physical advantages doesn’t reach full utilization). There’s graveyards of these cases.

There are 6’6 bums in the NBA, top draft picks, that had less of a career compared to say a 5’3 Mugsy Bogues.

In Basketball, part of your greatness is how well you optimized for the cards dealt to you.

Tech is not that much of a meritocracy like sports, it’s a way to have a living. I’d almost say, the tech industry is not equipped to assess greatness. Since it’s literally people’s livelihoods, we won’t (and shouldn’t) try to analyze this. In sports, if you come out of a top school as a top draft pick, fans will eventually say ‘hey you are a top draft pick but you do about the same thing as someone that went way later in the draft’. We in tech won’t ever go ‘look Facebook, your app looks like the same shit everyone else builds’. It can’t hold up to that scrutiny.

Open source is probably the only place where you can objectively assess.

It’s not a competition, because if it was, and tech was a sport, fans would tear it up as to what is a 10x engineer. Speaking as a sports fan, they are savage.

Hard work is the essence of what produces so-called talent. It seems to my understanding that talent is more so the culmination of a chain of clever thinking which ultimately derives more understanding and natural association with XYZ, than a deterministic natural born trait. Albeit, there are most definitely physical strengths tied to DNA, it wouldn't be entirely far-fetched to claim that different people have different mental strengths.

However, hard work doesn't always produce greatness. It would be misleading to say that hard work is equivalent to all other hard work. It's not. It's synonymous with the phrase, "practice makes perfect." Yet the phrase should really be amended to "perfect practice makes perfect." Aimless, bad practice will led to aimless, bad results.

Mental focus and barricades are huge components as well. The greats are just as human as the rest of us. Comparison is mentally debilitating for everyone, especially if "talented" individuals have a higher rate of growth than the average. Yet I have to wonder, does the individual who "desires greatness" aspire to achieve validation for ego-boosting fictional accomplishments or truly wish to push the edge of their capabilities to their respective human limits? The former have no chance at putting their energy into prospective efforts. The latter may continue to develop and create something the world hasn't seen before, and or realize that they can do things they had believed they couldn't before.