Name: データサイエンティスト vs データエンジニア。どちらがあなたに向いているのでしょうか？ (Data Scientists vs Data Engineers: Which one is for you?)
Uploaded: 2021-01-14T10:22:44.000Z
Duration: 9 min 33 s
Description: VoiceTubeの動画で発音を聞きながら英語表現を覚えよう！学べる英語：

過去形

So there was a question on tech interview pro in our private group, and there was a question that I asked, What are your thoughts on career Outlook for Senior data scientists versus Senior Data Engineer.

仮定法&仮定法現在

If someone has a choice between the two rows for their next job advancement, which one would you suggest?

to 不定詞

Um, so I answered this and I kind of want to make a video talking about this question, too, because I have a lot of things to say.

And, um, as you know, I worked at Facebook back down.

But personally, I do feel that data engineers are sometimes a little bit like a second class citizen.

I mean, sometimes data scientists, too, but I feel like data engineers can feel like that a little bit more now.

My roommate is a did engineer, so don't tell him that.

But, um, I personally think in a product heavy company culture, I'd say that PM's or the first class citizen and then data scientists and then did an engineer.

If we're talking in the data space and the reason is because PM's they often are the ones who make executive decisions.

They're the ones who kind of owned the future on the product, right?

And then for that, they tend to ask the d s a lot for product questions like, Oh, what are years or retention?

And they have a lot of data asks, and in some sense, it's a little bit of, like bitch work because they just want to get it done.

But then they realize that Oh, you know this information, they actually don't care about it, But they just want to know, just because of their curiosity.

And it doesn't actually change the decision of the product that happens a lot.

And then for the D s to do those data ask, they need the data to be structure in a certain way.

So then, because of that, they asked that the east to do it right.

So as you can see, it's a chain of bitch work, right?

D s the data scientists, they don't get impact for helping PM's answer to curiosity.

They don't get impact from creating these temporary tables or temporary pipelines just to ask.

It's just Thio served us one task for the data scientists.

So that's why, um, you know, it's like the lower you are in the chain of bullshit and sorry, not bullshit.

But like the shitty task, the Maura tends to be not that impactful, right?

So I believe that politically being a D s is better than P and data engineer.

I also talked about, you know, politics at work a lot, and I think a lot of people misunderstood what I said.

There's lots of politics, but I didn't say that.

Politics is part of society when we have multiple people working for different things, having different incentives, politics is super important, and you should be extremely good at it because that is how you negotiate.

That is how you get together with a lot of people, have different incentives so that you can all commonly work on the same goal, right, or even if it's not the same girl.

You could work on different things such that it's mutually beneficial for each other.

Now the thing is, yes, there's a lot of like, crappy work going down.

And then that's usually what D s managers and D managers do.

They tried to fend off these ridiculous requests so that their report can be a lot more productive in real work.

So I have a lot of friends that are deal engineers.

And to be honest, they say that you don't really like that engineers that much because, um because in some sense it's not intellectually challenging, especially at a big company where a lot of these hard distributed data problems are already solved.

Right, So you're not gonna be doing any spark or Hadoop jobs and stuff like that.

These air, these air abstracted the thing you do as a d.

His you mostly right sequel based stuff, right?

Sequel based pipelines, because that's the easiest way to write it.

And they have abstracted everything such that makes it easy.

And then what you have to do is you just have to organize your product areas data, and you have to monitor these detail, pipelines and all of these things.

They're basically like, um, like sequel kind of work or just like maintenance and using the the tools that we've already been that have already been built right.

And you also get paid less than a software engineer.

So I think a lot of people, a lot of data engineers, at least that Facebook I don't know about any other companies.

They're a little bit more dissatisfied because their job is that's intellectually stimulating and to get less equity.

So I think that's one of the biggest concert did Engineer.

But I am biased because I was a data scientist, so I might not know a lot of things about eight engineers Cool.

So for data science analytics and I mean analytics, especially for people who want to get to data science.

And they don't have a master of PhD in something very particular like a machine learning.

Usually you're gonna be ending up doing data science analytics.

Now most people come into this thinking that it's like big data or like machine learning kind of thing.

But they quickly get disappointed because they realize that it's not that's that's not what it is right, especially if you're a new grad coming from school.

Unfortunately, you're not gonna be doing any innovative machine learning algorithm stuff.

You're not gonna be inventing anything, right?

So But I do personally think that data science analytics is super rewarding, right?

Like, um, like, the thing about new grads is they focused too much on the technical part of work.

You know, day focused too much on try and grow in terms of their technical skills, which which makes sense, because in the beginning, you need technical skills.

You need these foundational skills to be useful in work to do stuff right.

But to be honest, the technical part is usually the easiest part of the job at a large company.

At least, like, um, if you want to be a data scientist than you're the person that loves to think about the product loves to think about what to do next.

Four to product, and you want to be the expert on the product because you love digging into the data, finding, finding insights and then using these insights to help the company or help the product become better and increase those metrics, right?

And yeah, so that is actually what a data scientist is supposed to be.

You are the core of the strategy and direction of the product, and your superpower is that you're able to transform raw data into strategic decisions.

It's kind of like, you know, when we talk about programmers, they transform Red Bull into workable software, right?

So I do think that it doesn't really matter what job you choose, because the people who advance the most the people who succeed at work are people who do not restrict themselves to dirt job function right?

Think like What can I currently do with my set of skills that would have the most impact on my team, like that's what you're supposed to do.

That's how you're supposed to choose which job you want to do.

It depends on what skills or interests you have the most.

But then, once you're at the job, do not restrict yourself right, because the people who do not restrict themselves or the ones who um, who succeed the most.

For example, a few examples, like engineers with data analytic skills.

Where you can do is as an engineer, you could find user problems yourself big into the data fine issues of your product, right?

And then now you know that there's a great future opportunity because of your day of skills.

And then you could argue for it, like with your with their P m teem with your product team and then just build it, and then that would have a lot of impact.

And then, if you're a PM with, uh, with engineering skills, then you can prototype in N V P.

You can or you can create experiment for a future and then just build it out and then test it out and then see that there's positive metrics or that it's good for the Are you found product market fit with that experiment with that 1% experiment and then boom, There you go.

You you're able to creating M V P and invalidate your hypothesis and then have this project up in the high priority list on your old math high impact for PM's as a data scientist, if you have PM skills, then essentially you can set the vision and also the road map for your team because you know you're able to have the data skills to back up all of your arguments for what should be the high priority stuff to do for the company or for your team.

And then you're able to privatize all these features because, like I said, have these data skills and then because you have this leadership skills or these P m skills I said You have, you're able to launch your team to success because of that.

So even if you have a weak PM, you could carry the team.

So I mean, in conclusion, pick a job according to your skills and but never restrict yourself on your job title.

 They pay you to invest in you so that in the future you could make a huge difference. 