so there was a question on tech
interview Pro in our private group and
there was a question that asked what are
your thoughts on career outlook for
senior data scientists versus senior
data engineer if someone has a choice
between the two roles for their next job
advancement which one would you suggest
so I answered this and I kind of wanted
to make a video talking about this
question too because I have a lot of
things to say and as you know I work to
add facebook back then I used to be a
data scientist I think I could say it
now we'll see 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 data engineer so don't tell him
that but I personally think in a product
heavy company culture I'd say that PMS
are the first-class citizen and then
data scientists and then data 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 there are the ones who kind of
own the feature on the product right and
then for that they tend to ask the des a
lot for product questions like Oh what
are user retention so can we cut this by
by a country oh can we cut this by this
and they have a lot of data asks and in
some sense it's a little bit of like
[ __ ] work because they just want to get
it done but then they realize that oh
you know this information they actually
don't even 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 DES to do those
data ask they need the data to be
structured in a certain way so then
because of that they asked the de's to
do it right so as you can see it's a
chain of [ __ ] work right yes the data
scientists they don't get impact for
helping PMS answered your curiosity des
data engineers they don't get impact
from creating these temporary tables or
temporary pipelines just to ask as just
to
just one task for the data scientist so
that's why you know it's like the lower
you are in the chain of [ __ ] and
sorry not [ __ ] but like the shitty
task the more it tends to be not that
impactful right so I believe that
politically being a des 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 they think that oh no it's
terrible there's lots of politics but I
didn't say that it's a bad thing
politics is part of life politics is
part of society when we have multiple
people working for different things
having different incentives politics is
super important then you should be
extremely good at it because that is how
you negotiate that is how you get
together with a lot of people with
different incentives so that you can all
commonly work on the same goal right or
even if it's not the same goal you could
work on different things such that it's
mutually beneficial for each other
cool now the thing is yes there's a lot
of like crappy work going down and then
that's usually what DS managers and DD
managers do they try to fend off these
ridiculous requests so that their report
can be a lot more productive in real
work right okay so let's talk about des
or de versus des so I have a lot of
friends that are data engineers and to
be honest they say that they don't
really like data engineers that much
because 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 going to be
doing any spark or Hadoop jobs and stuff
like that these are these are abstracted
the thing you do as a team is you mostly
write sequel based stuff write sequel
based pipelines because that's the
easiest way to write it and they have
abstracted everything such that it makes
it easy and then what you have to do is
you just have to organize your project
areas data and you have to monitor these
ETL pipelines and all of these things
they're basically like like sequel kind
of work or just like maintenance and I'm
using the the tools that we've already
been that
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 have Facebook I don't
know about any other companies they're a
little bit more dissatisfied because
their job is less intellectually
stimulating and they get less equity so
I think that's one of the biggest cons
of data engineer but I am biased because
I was a data scientist so I might not
know a lot of things about data
engineers cool so for data science
analytics and I mean analytics
especially for people who want to get
into data science and they don't have a
master PhD in something very particular
like in machine learning usually you're
going to 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 going to be
doing any innovative machine learning
algorithm stuff you're not going to be
inventing anything right you have to be
a little bit realistic so but I do
personally think that data science
analytics is super rewarding right like
like the thing about new grads is they
focus too much on the technical part of
work you know they focus 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 if
you want to be a data scientist then
you're the person that loves to think
about the product loves to think about
what to do next for the 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 should teach
it decisions that's your job it's kind
of like you know when we talk about
programmers they transform redbull into
workable software right it's kind of
like that 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
death job function right and you should
always be striving to 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 interest you have the
most but then once you're at that job do
not restrict yourself right because the
people who do not restrict themselves
are the ones who who succeed the most
for example I wrote a few examples like
engineers with data analytics skills
where you can do is as an engineer you
could find user problems yourself dig
into the data find issues of your
product right and then now you know that
there's a great future opportunity
because of your data skills and then you
could argue for it like with your with
their PN team 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 a with engineering skills then
you can prototype an MVP you can or you
can create an 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 create an MVP and
then validate your hypothesis and then
have this project up in the high
priority list on your roadmap
high-impact for PMS as a data scientist
if you have PM skills then essentially
you can set the vision and also the
roadmap for your team because you know
you're able to have the data skills to
back up all your arguments for what
should be the high priority the stuff to
do for the company or for your team and
then you're able to prioritize all these
features because like I said have these
data skills
and then because you have this
leadership skills or these PM 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 so take that opportunity to
invest in yourself so that you could be
a beast at everything
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