How He Got $600,000 Data Engineer Job
Summary
TLDRIn this video, Zach Wilson shares his journey from a data analyst to a data engineer at major tech companies like Facebook, Netflix, and Airbnb. He discusses his salary progression from $80k to $600k, the unique work cultures at these companies, and the importance of data engineering skills like SQL, Python, and Apache Spark. Zach also addresses the impact of AI on data engineering roles and advocates for learning tools like Airflow and Databricks. He advises aspiring data engineers to focus on data modeling and machine learning for job security and provides resources to start a career in data engineering.
Takeaways
- đ Zach Wilson's career started as a data analyst but evolved towards data engineering after realizing the rapid growth of data.
- đ Zach was initially attracted to Hadoop, viewing it as a critical skill to learn early in his career.
- đ He found the corporate environment at Teradata too restrictive compared to the startup culture he preferred.
- đ Zach's experience at Facebook (Meta) was significant for his growth as a data engineer, where he utilized tools like Hive and Dataflow.
- đ Netflix offered Zach a high salary upfront but also induced imposter syndrome due to the highly skilled team environment.
- đŒ Zach's salary progression from 80k to 600k is attributed to joining big tech companies and effectively negotiating his worth.
- đŒ He advises never to give the first number in a salary negotiation to avoid potentially lower offers.
- đĄ Zach emphasizes the importance of learning languages like SQL and Python, tools like Spark and Airflow, and skills in data modeling for aspiring data engineers.
- đź The future of data engineering might see roles transformed or replaced by AI and low-code/no-code tools, but roles requiring nuanced expertise like machine learning will remain secure.
- đ Zach misses the collaborative environment of corporate life but enjoys the autonomy and accountability of entrepreneurship.
- đ For those looking to break into data engineering, Zach recommends his blog and the 'Data Engineer Handbook' GitHub repository for comprehensive resources.
Q & A
What was Zach Wilson's initial career role before becoming a data engineer?
-Zach Wilson initially started his career as a data analyst, specifically a 'Tableau guy', which involved working with data visualization tools.
Why did Zach Wilson decide to transition from data analysis to data engineering?
-Zach Wilson became bored with his data analyst role after mastering Tableau in about 9 months and decided to transition to data engineering after reading a statistic that 90% of the world's data had been created in the last 18 months, which made him realize the potential in the field.
Which company did Zach Wilson join after recognizing the growth in data and why?
-Zach Wilson joined a startup called Think Big Analytics after recognizing the significant growth in data, as he was drawn to the idea of working with big data technologies, particularly Hadoop.
How did Zach Wilson's career progress from Teradata to Facebook?
-Zach Wilson's career progressed from Teradata, where he worked for about 6-7 months in a very corporate environment, to Facebook after he left Teradata due to dissatisfaction with the corporate culture. He then moved to Washington DC for a brief period of software engineering before landing a data engineering role at Facebook in San Francisco.
What was the approximate salary progression for Zach Wilson from his first job to his role at Netflix?
-Zach Wilson's salary progressed from 80k at Teradata to 185k after joining Facebook, and then to 365k when he joined Netflix, where he was able to negotiate an increase to 550k within his first year.
What was the significant mistake Zach Wilson made during his salary negotiation at Netflix?
-Zach Wilson's significant mistake during his salary negotiation at Netflix was accepting the initial offer of 365k without doing proper research. He later found out that the median salary for his team was 500k, indicating he could have negotiated a higher starting salary.
What cultural differences did Zach Wilson experience between Facebook, Netflix, and Airbnb?
-Zach Wilson experienced a very collaborative culture at Facebook, which he felt was sometimes too collaborative. Netflix had a more mature and less intrusive work culture, with team members having established personal lives. Airbnb was not explicitly described, but he mentioned that both Google and Airbnb were known for their good work-life balance.
How did Zach Wilson overcome his imposter syndrome at Netflix?
-Zach Wilson overcame his imposter syndrome at Netflix after about a year, particularly after achieving a significant success with a database project, which made him feel that he truly belonged in the role.
What advice does Zach Wilson give for someone looking to get into data engineering?
-Zach Wilson advises that to get into data engineering, one should learn critical languages like SQL and Python, become familiar with tools such as Spark and Airflow, and understand data modeling. He also recommends his blog and the 'Data Engineer Handbook' GitHub repository as resources.
What is Zach Wilson's perspective on the impact of AI on data engineering roles?
-Zach Wilson believes that while AI will change some data engineering roles, especially those involving simple tasks, more nuanced roles involving machine learning or master data management are safe. He sees AI as a tool that can help solve problems faster but doesn't see it replacing the need for expert data engineers.
What is the 'Data mesh' architecture mentioned by Zach Wilson and why is it significant?
-The 'Data mesh' architecture is a new approach where business owners can manage and maintain their own data pipelines, potentially reducing the need for traditional data engineering roles. Zach Wilson believes it could become more successful with the advancement of tools like LLM (Large Language Models), although he has seen it fail in companies due to lack of proper tooling.
Outlines
đ Introduction to Data Engineering with Zach Wilson
The video begins with the host welcoming Zach Wilson, a data engineer, to the channel. Zach is introduced as someone who has been in the industry since its early days, starting as a data analyst and moving into data engineering roles. The conversation starts with a discussion about the evolution of data engineering as a field and Zach's personal journey, including his time at various companies like Teradata, Facebook, Netflix, and Airbnb. The host expresses excitement about discussing data engineering with Zach, hinting at the valuable insights he is expected to share.
đŒ Zach's Career Progression and Experiences at Major Tech Companies
Zach shares his career progression, starting from an initial salary of $80k to eventually reaching $600k. He discusses his experiences working at different tech companies, highlighting the collaborative culture at Facebook (Meta), the all-cash compensation structure at Netflix, and the work-life balance at Airbnb. He also talks about his salary negotiations, emphasizing the importance of research and not accepting the first offer. Zach reflects on the differences in company cultures and how they affected his work and personal growth.
đ Zach's Transition from Corporate to Entrepreneurship
Zach talks about his decision to leave Netflix and his subsequent journey into entrepreneurship. He shares his initial struggles with identity and purpose after leaving the corporate world, and how the pandemic influenced his transition. He discusses his new role as a content creator and entrepreneur, focusing on data engineering education. Zach also expresses some of the things he misses about corporate life, such as working in a team and the structured support systems, while also appreciating the accountability and freedom that come with being an entrepreneur.
đ Advice for Aspiring Data Engineers and the Impact of AI
Zach provides advice for those looking to enter the field of data engineering, emphasizing the importance of learning key languages like SQL and Python, and tools like Spark and various workflow orchestration systems. He also stresses the significance of data modeling skills. Regarding the impact of AI, Zach believes that while some data engineering roles may evolve or be replaced due to advancements in AI and low-code/no-code tools, there will always be a need for specialized expertise in areas like machine learning and master data management. He predicts that the data engineering landscape will continue to change with the adoption of new architectures like Data Mesh.
Mindmap
Keywords
đĄData Engineering
đĄTableau
đĄHadoop
đĄCollaborative Culture
đĄImposter Syndrome
đĄSalary Negotiation
đĄData Mesh
đĄLLM (Large Language Models)
đĄSpark
đĄData Modeling
Highlights
Zach Wilson's career transition from data analyst to data engineer.
The rapid evolution of data engineering as a field.
Zach's experience at Facebook, Meta, Netflix, and Airbnb, highlighting the cultural differences.
The importance of collaboration and work-life balance in tech companies.
Zach's salary progression from $80k to $600k and the strategies behind it.
The significance of negotiation in salary increases, especially at Netflix.
Zach's entrepreneurial journey and the transition from corporate to content creation.
The role of AI in data engineering and the potential for job displacement.
The concept of Data Mesh and its impact on data engineering roles.
Zach's advice for aspiring data engineers on essential skills and learning paths.
The importance of data modeling in data engineering.
Zach's thoughts on the future of data engineering roles in the context of AI and automation.
The benefits and challenges of the entrepreneurial path compared to corporate roles.
Zach's recommendations for resources to learn data engineering, including his GitHub repository.
The impact of low-code and no-code tools on the future of data engineering.
Zach's final thoughts on the importance of specialized skills in data engineering.
Transcripts
median 50th percentile person on my team
was making 500 I do think that there's
definitely some data engineering roles
that are going to be uh replaced hi
everybody welcome back to another video
in today's video we're going to talk
about data engineering with none other
than Zach
Wilson Zach welcome to my YouTube
channel I'm so excited to have you thank
you I'm very happy to be here Zach is
currently in Seattle and we just hosted
a Meetup together and after this Meetup
we thought we would create some content
for all of you so thank you Zach for
being here and I'm so excited to talk to
you today yeah it's going to be great so
let's jump in cuz I am pretty sure when
you became a data engineer data
engineering did not exist yeah like I
actually started my career as like a
data analyst kind of Tableau guy that's
like what I did at my very first job and
I recognized at least for that role back
then this was like 2014 like 10 years
ago and I recognized I was like I don't
want to be doing this like for I'm done
like CU I feel like I mastered Tableau
in like 9 months and I was like this is
as much as this tool can do like you
can't go further and so that's when I
got kind of got bored of that role and I
was like I need to go somewhere else and
then like I read a stat stat was that
90% of the world's data had been created
in the last 18 months I the same Stu
yeah I'm telling you and I was like damn
I need to get in there right and like so
that's when I joined this startup called
think big analytics okay and the other
one I got obsessed with like the yellow
elephant Hadoop right that's like I was
just very drawn to that elephant I was
like I need to learn this elephant I
remember I made a post back then where I
was like this elephant is about to
become my entire life right and I did
not understand like how true that posted
end up being but like then I worked at
think big for a while uh I was not there
very long because what happened was
think big got acquired by terata and
terod is like this corporate company and
like you had to like wear like freaking
like business like like a button- down
shirt like it was like very corporate
very corporate I was there I was there
for like 6 months 7 months and I was
like I'm like I hate it here I don't
like this this is not the company I got
hired for I thought I was joining like
some hip startup and yeah and I was like
this is not it for me and left I left
and did like some software stuff for a
little bit like 6 months where I
literally like went from Utah to
Washington DC did software engineering
for 6 months and then I got a job at
Facebook doing data Engineering in San
Francisco so I moved again and yeah 2016
was a messy year for me where I was just
driving all the time I felt like that's
all I was doing that year but then once
I got in at Facebook like that's when I
felt like I actually was like doing real
data engineering and I was like getting
into it more like that's we using like a
lot of Hive back then it was a lot of
Hive and this thing called Data swarm
which is essentially airflow meta
Netflix Airbnb and then entrepreneurship
I want to talk about Netflix and Airbnb
and meta like how was the culture
different between the three oh they're
very different So like um Facebook one
of the things about is great about
facebook/ meta is that like it's very
friendly everyone is very collaborative
in some ways I actually felt like it was
too collaborative right where I was like
I'm just trying to get my work done and
then because like then people like hey
Zach I have a question hey Zach I have a
question I'm like dude I get 30 minutes
a day to work on things right like and S
familiar and so like uh and then Netflix
was not as much that way right well and
one of the other things at least for me
in my journey there was like I got hired
at Netflix and when I was really young
especially for Netflix cuz so when I
joined Netflix I was 24 and the next
youngest person on my team was 35 so
like there was an 11year gap between me
and the next youngest person on my team
and so like for me definitely at Netflix
I like the culture was still very
collaborative but also people like had
their own lives their own families they
have all their own like stuff like and
all that stuff and like I like I for me
in my relationship to work in Netflix
though I just had imposter syndrome the
whole time because it was just like I
was like I don't belong here like I'm
working with all these really talented
people like why am I here too right and
so I like and I just felt like I'm like
I have to work to like prove that I
belong here right yeah for sure did you
get to that point then like where you're
imposter and went away yeah it was like
um I felt like it was after about a year
when I like when I especially when I got
like my first big win with this like
database thing I was working on like I
was like okay okay I belong here I
belong here for I love that I love that
I feel like especially like first year
for anybody like who's just starting out
it's always like cuz I had a similar
experience first year such a big
imposter syndrome and then after
delivering some stuff I'm like no I
think I got it like I can do it so I
know you started when you got your first
job you started at 80k M how did that
progression go from 80k to meta Netflix
and then
Airbnb 600k that's like unheard of so
how did you do that how do people do
that yeah it's a journey like and it was
actually something that I even was
surprised by myself especially like when
I got that job making 80k at terod dat
back when I had a wear a button- down
shirt and I hated it like I my dream for
myself was like I'm like dude if I can
get to 200 I will be such a baller I
will be like this is going to be my life
and I thought 200 was like when I'm 35
right when I'm like 10 years deep 15
years deep that's when I'm going to hit
200 I learned a couple things about it
like a big thing about it is like you
need to get into the system in big Tech
that's a big part of it is like if you
can get that experience in especially
meta specifically I feel like they do a
great job at like investing in their
Engineers better than Amazon I feel cuz
Amazon really like holds that senior
promotion as like a you got to really
like work incredibly hard to get senior
whereas a meta like they like encourage
people more like my friend Ryan right he
did from he went from Junior to staff at
meta in three years he just got promoted
every wow you have to do that at meta
right they push you they really do like
I mean it's good and bad I feel like cuz
it's like it's good like if you want to
grow but it's bad if you want like a
life if you want to CH if you want a
coast meta is not the place for you no
that's
Google or Airbnb or Airbnb both yeah
both are really great so for me like
when I got in at Facebook so I went from
80 and then like a year later cuz I was
making adk in 2015 at terod data and
then when I got in at Facebook it was
like 185 only actually ever been
internally promoted one time in my
career one time and it was at Facebook
at Facebook cuz I got hired as a junior
engineer at Facebook and I got promoted
to L4 right that's the only promo I've
ever actually gotten so going to L4
bumped me to like 230 is right and then
I was like I'm done I want to be a
software engineer and I'm like then I go
to Netflix Netflix is crazy Netflix is a
crazy company they give you all cash
yeah they give you all cash just up
front here you go we're going to pay you
as much as like the market will give you
right and I actually made a mistake I
made a big mistake at when I got my
interview at Netflix I probably lost
$100,000 from from this mistake so I
accepted my initial offer at Netflix I
accepted was 365k cuz going from 220 to
365 I was overjoyed right like I
actually learned though I learned that
the median for my team the not the
highest paid guy the median 50th
percentile person on my team was making
500 right so and I didn't even know
about that I just was like so excited to
get in at Netflix I learned about that
about six months deep into my into my
time were they expecting you to
negotiate is that why they gave you
offer yeah I thought I was being smart
so what I said was like I won't accept
anything less than what an E5 makes at
meta right and they gave me exactly that
right you basically yeah and like but
they they would have given me more than
$100,000 more like had I freaking like
actually like done my research that's
why you never give the first number
never never never and good news though
was about that was that like uh
ultimately after about 6 seven months at
Netflix I got on to a very like senior
like borderline staff level project like
this graph database project and I was
able to negotiate up I was able to
negotiate up in my first year at Netflix
from 365 to 550 right and then I was
like damn those like I've never had a
raise like that before I was like this
is a crazy raise crazy especially if
it's all cash yeah it's all cash right
your bank account is probably freaking
out taxes
man don't talk to me about taxes on the
topic of tech salary I analyzed
entry-level data scientist salary using
this powerful AI tool that you should
know about it's called Julius AI think
of it like a data analyst or a data
scientist that is available at your
fingertips that can write python code
for you and yes if you're wondering it
is 10 times better than Chad GPT
Advanced analysis don't believe it let
me show you so the first thing we're
going to do is find a file that we're
going to use for data analysis next
thing I do is I go to Julius so here I
have an option to either upload the file
or connect it to Google Sheets next
thing I'm going to do is I'm going to
ask it to filter the data with zero or
one year of experience and the coolest
thing is that it writes code while doing
it so if you're just learning python or
R yes it can do R it has R capabilities
as well so if you want to learn this is
actually a great place to Learn Python
and R while it's doing the data analysis
for you the next thing I'm going to do
is I'm going to ask you to create
visualization of data scientist salary
by company in Seattle and it has created
the visualization for me basically
looking at this plot I can tell that
Uber pays the highest amount of
entry-level salary to data scientist
followed by Amazon then Zillow and then
Facebook so as you can see that it has
not only created a visualization for me
it has also given me additional prompts
and things that I can ask you this took
me less than 3 minutes imagine if you
had access to it actually you do you can
try Julius AI for free using the link in
description thank you Julius AI for
sponsoring this section of the video now
let's go back to Zach's salary
progression to 600k M 20 19 2020 like
they Netflix changed they changed a lot
so the big thing they did so before they
had two orgs they had data engineering
and infrastructure and then they had
data science and algorithms two orgs
right and then what they did in 2019 is
they just they fired the data
engineering leaders all of them my VP my
director my manager the whole chain just
right acts right and then they were like
oh yeah we're not hiring him we're not
going to rehire those leaders you guys
are all now part of data science and for
me I was like I don't know about that I
don't think cuz especially because the
leaders that they got they let they let
go I really believed in MH so I became
very disillusioned about working there
because I was like also just living in
fear cuz I was like okay these people
who I really these leaders who I looked
up to like they taught me a lot like I
feel like they were like some of the
best people I've ever worked with and
like they're getting let go and I didn't
really get it and I'm like and I lost
all motivation to work there so I was
like I'm done I'm not going to work here
anymore and then I left and I just like
went on some soul searching for a bit
cuz I like I realized at that point that
work was my life that was my entire life
that was everything right and so like
after I quit there was some months there
where I was like who Am I who am I as a
person who is Zach right I was like I I
really lost in touch with like my own
like person and then 2020 pandemic that
year was wild that year was so wild and
then ultimately I got in back at Airbnb
like 9 months later right when I started
uh at Airbnb that was also when I
started making content CU I knew cuz
when I quit Netflix it's the work life
balance it's for it's true for me too
yeah yeah for sure gole content yeah and
for me when I quit Netflix I had a
vision for myself that I was going to be
a Creator cuz I vividly remember when I
quit Netflix the day I quit when I was
like I we had like a going away lunch
and when I was gone I was like when I
like I gave a peace sign to all my
co-workers and I'm like you're going to
see me again everywhere that's what I
said right I had a vision for myself
then and I wish I would have overcome
some of that like depression and sadness
in 2020 to start earlier but I mean I'm
happy I started when I did cuz like I
was able to get in it and like uh just
start growing cuz it's all about like
just getting that initial momentum of
like I'm going to do this this is my new
thing right then after a while it's like
compound interest just keeps you
motivated after a while you know so 600k
at Airbnb yeah for sure and that's when
you decided to leave all together and
when was it this was about a year ago
2023 okay so for a year you have been an
entrepreneur primarily focused on data
engineering what like data engineering
teaching uh that's my big Focus yeah
okay and how is that going do you miss
corporate Ah that's a great question I
miss some things about corporate yes I
mean I actually gave an interview a
couple weeks ago I had my own doubts
like the entrepreneur journey is very
full of doubts itself when you have
employees and you need to make payroll
and you have like all of this like
pressure to like you have to make sales
it's like if you don't make sales you're
going to have to fire people right it's
like there's like a lot of like
consequences like when you're an
entrepreneur that are like when you're
an engineer you just don't think about
them you just think about solving the
problem in front of you I feel like for
me the things that I miss the most about
corporate is the big one is working on a
team actually like in an office I missed
the office actually a lot that was
actually one of the things that like I
was like kind of seeking out where I'm
like I need I need to like be around
people and like work on problems
together uh I think that's the big thing
I miss I kind of also miss just like
being able to like not think about food
and have to like having to like feed
myself three times a day it's a lot of
work when I worked at meta it was like
they would give me breakfast lunch and
dinner and it's just like automated and
I don't have to think about it right
that's also really nice so I know that's
a very Tech bro take right there but
that's like it is what it is right no
it's a fair take for F I'm there the
things I don't miss are like feeling
like uh I'm in a situation that is
outside of my control like I feel like
one of the things that's great about an
entrepreneur is it's extreme
accountability like if you are
successful it's because of things you
did if you aren't successful it's
because of things that you did or didn't
do so talking about data engineering uh
really quick if somebody wants to get
into Data engineering what should they
learn where should they start what's
your advice to them that's great uh I
think there's a couple things there like
you have to learn the languages
languages are the like critical It's
like because if you don't know the
languages you can't speak the stuff
that's necessary so you SQL and python
are going to be the most important if
you're trying to be more like in the
like Cutting Edge space then maybe learn
like Scala or rust rust in particular is
probably like in the future like give it
like five years it's going to be bigger
um and then uh then you need to know the
tools the tools are like spark airf flow
but there's like 5 million competitors
for airf flow now there's like air flow
Mage prefect Dag data bricks workflows
Azure workflows there's like a there's
there's a lot of freaking ways to
schedule a job right so and just
learning Kon and how to schedule a job
and then picking one flavor of that and
then spark is so important though I
think that spark is going to keep being
bigger it's actually getting more
adoption which I think is crazy because
it's been around for so long it's been
around for like 12 years and it's like
and it and it still feels like it's in
the you know the early part of the
adoption curve it hasn't like it hasn't
leveled off yet it's still like it's
still growing which
boggles my mind I feel so lucky that I
like caught that in 2016 where I'm like
I'm going to learn spark and it's going
to be awesome and I was
right that's awesome yeah I think and
then the last important skill though is
data modeling because you got to
remember that like just because you have
a pipeline that is productive and good
and efficient if it produces data that
is annoying to use then you you drop all
your value at The Last Mile right and
that that last mile of like what is the
contract between you and the data
scientist right like what is like what
are the name what do you name your
columns make sure you don't have stupid
column names it's like there's a lot of
these things are actually very basic but
a lot of people get them wrong you like
you'll see it in the wild even in big
Tech at companies where they pay people
500k they still get it wrong and so like
getting that like modeling stuff right
is very important and I think those are
going to be the three ones like so
orchestration spark and modeling are
going to be the big things and then like
where to start I mean my blog is pretty
good so blog. dateng engineer. a lot of
things there I have a lot of road maps
that go a lot more in detail than the
last two minutes that I've said but like
that's going to be a big one and then
there's like a big another thing the
Google Google the data engineer handbook
so I have a GitHub repository that has
over 8,000 stars that has all the
resources that you need to get into Data
engineering and yeah I built that like a
couple months ago in the description
awesome yeah so that's how I'd say to
get in for sure okay awesome cool last
thing and then we'll close the video um
AI yeah should data Engineers be worried
about AI I mean ah great question I
think less worried than the current
market makes it feel like it sometimes
it feels like you know there's like
those things like Devon that are out
there where it's like look this thing is
can just do your job and it's like no it
can't like like I mean but like there's
uh there are spaces right like I feel
you need to be able to leverage AI in a
way like for example for me like when I
uh have been building things like uh
like you can solve problems like so much
faster like if you just use AI like and
like there it it's like a I think of it
more of like as like a super Google it's
like a Google that gives you a an answer
that might just be right and you can
paste it in and it just works right but
not all the time obviously you still
need to know the nuances of things but
like I think as a tool it's great and I
think uh there are going to be roles I
do think that there are I mean I I do
think that there's definitely some data
engineering roles that are going to be
uh replaced and uh changed or like
they're going to morph right because
like especially with like the
combination of llm generated stuff and
then you have like uh low code no code
tools you know like five Tran and all
those other kind of tools there and when
those things marry and then you have
like people who can create pipelines
with like a sentence and five Tran then
like there's going to be a lot of those
roles that right now are done like by a
data engineer that don't need to be done
by a data engineer and that's why
there's this new architecture that's
coming out called Data mesh where you
have people who like cuz the data
engineering pattern itself is in some
ways kind of like a middle
right because like you as a data
engineer need to talk to the business
and talk to the analysts and understand
try to understand all the pieces of the
puzzle when like you aren't as close to
the business yourself like you are like
kind of in the middle ultimately if the
the business owners the people who are
solving those problems directly like I
don't it might be the PMS it might be
some other people if those people can
write the pipeline themselves and manage
and maintain the pipeline themselves
it's a better ownership pattern it's a
more stable ownership pattern as well
and that's what data mesh is looking to
solve and I ultimately think that data
mesh will work even even though I've
seen it fail I've seen it fail a bunch
of times in companies but I think it's
because we don't quite have the tooling
yet and llm is one of those things
that's going to make that more likely to
happen yeah but for a lot of the data
engineering roles the ones that are not
just like a select query and an
aggregation or like something that's
just a little bit more like anything
doing with like Master data or machine
learning or anything like in those areas
those roles are very safe because those
roles are just so nuanced and so
difficult to get the the model right
that like you need to have that person
with that expertise so that's where if
you want to like have a safer data
engineering role lean more into uh like
machine learning stuff lean more into
Master data management and because those
roles are very safe I do not see those
roles going away anytime soon awesome
cool where can people find you yeah I
mean I'm on YouTube data with Zach is
probably going to be the best place you
can also find me anywhere um on the
internet my username is exactly e CZ l y
and that's yeah those are going to be
the main places awesome well thank you
so much this is awesome and hopefully
people watching they learned something
watching from this video so thank for
sure glad to be here yeah awesome all
right bye so good
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