why is data engineering better than data scientists role so in this video I'm going to
talk about like four reasons why data engineering is better than a data scientist Rule now before
I jump into the video I do want to say that this video is not to batch any job family this is based
on my personal experience my personal opinion after having worked in both of the job families
you may have a different opinion completely fine completely okay to have a different opinion
I'm just here to share my POV on what are the four reasons why data engineering draw family
is better than data scientist job family reason number one data engineers build tangible things
while data scientists A lot of the times they are building models that live in the back end behind a
product behind a feature or analysis like that you can't really it tells you a story it's more of a
storytelling it's nothing tangible versus a data engineer is building actual product like their
tables that they are building their pipelines that they're building that somebody can go and
see and use so that is like one of the biggest key differentiator between the two jaw families and
there's a lot of times when I miss building the tangible piece of data engineering and I'll tell
you why because when I worked as a data engineer it was very easy to prove value of my work because
I will build Theta pipelines and at the end of my year if I need to prove my impact I can say
like I build two data pipelines which encompasses X number of tables these tables are being used by
X number of people and they are getting value basically I'm able to tell a story and actually
point to physical things that I have done and my work has contributed to them whereas in working
as a data scientist I actually had to and I still have to a lot of the times figure out what is that
impact and that is a very tricky situation because like you build a model it goes into production
let's say if it's trying to improve any front facing customer metric let's say customer more
attention it's not that straightforward to figure out like what is that what your model specifically
attributed to improving customer retention let's say so you can put that number let's say if your
model improved customer retention by two percent you can say like if you've improved by two percent
but there's like nothing tangible that somebody can go and see whereas a data engineer they have
data tables they are Tails built they have the usage somebody can go and easily look
what are those tables and what are the usage of it and who's using it and they can go ask
those people questions like so so I think like that's a big difference and there's something
satisfactory about building tangible things which is something that I really really miss
in my current job working in the data scientist domain the second reason why data engineering is
better is the career ladder is more structured I've talked about in so many of my videos how
data scientist career letter is such a big mess it means something different from company to company
whereas when you talk about data engineering data engineering ladder is actually more stable
and the definition is more well understood among different companies you start as a junior data
engineer you become mid then you senior then you principal there's actually a clear career
trajectory for data engineer later there's clear skill set whereas on the data scientist side it's
actually a total mess where one company thinks that a data scientist means something completely
different than company X and the skill set could be very different and it could be a mumbo jumbo
of everything I think what I'm trying to say is like for data scientists very likely that not your
skills are not going to be that transferable and the career path we haven't seen a lot of principal
data scientists in in my experience I personally haven't seen a lot of principal data scientists
what I have seen is a lot of people transitioning out of the data scientist to our family versus on
the data engineering side the career trajectory is well way more defined and very similar to a
software engineering of which same the third which I think is pretty obvious is like data engineering
is more of a back-end role you don't deal with business very often versus a data scientist role
so if you are the type of person who don't like dealing with business who like staying in the
back end and who wants to like build things and stay behind the scenes like I think this is a
great role this would be something that one person may prefer another doesn't for me for example one
of the reasons I transition out of the data's engineering role was like it was like super in
the back end and I wanted to do more data science and I wanted to work closely with the business and
product so depending on that like if you are the type of person who likes staying in the back end
who doesn't want to deal with business then like data engineering is a good career letter for you
and the last is pay is very similar to a data scientist if not better than a data scientist
and I would say like data engineering queer ladder is more closer to a software engineer
than a data scientist and that also reflects in the data engineering salaries now the downside
is that a lot of companies don't value their software engineers and data engineers at the same
level but theoretically and from the skill set perspective software engineer and data Engineers
are have much more similarity and data engineers you get paid more in some companies or at least
at par as the data scientist anyways those are the four things that I could think of that why
I think data engineering job family is better I would love to know your thoughts why do you
think data engineering job family is better than data science data scientist rule or vice versa all
right with that thank you so much for watching today's video I'll see you in the next one bye