ABOUT THIS EPISODE
Victor Fernandes is a data platform engineer from Fortaleza, Brazil — and his path to get there is unlike anything I've heard before. Seven and a half years automating power plants and steel factories, then a single phone call in 2021 that pulled him into the data world. We talk about what it's like building for U.S. teams from Northeast Brazil, the cultural shock of his first American standup, and how he thinks about AI on production systems. You'll come away with a very different picture of what Brazilian engineering talent actually looks like up close.
🎧 Host | Brian Samson – Founder of 💻 Plugg Technologies
🔗 https://www.linkedin.com/in/briansamson/
🎙️ Sponsored by Plugg Technologies – Connecting U.S. companies with top-tier software developers across Latin America.
🌐 https://www.plugg.tech
🌎 Follow The Nearshore Cafe Podcast
🎵 Spotify: https://open.spotify.com/show/6KYcgpmN77fJm6B25469B8
🎙️ Apple Podcasts: https://podcasts.apple.com/us/podcast/the-nearshore-cafe/id1775525954
💼 LinkedIn: https://www.linkedin.com/company/the-nearshore-cafe
🌐 Website: https://www.nearshorecafepodcast.com
🔗 All links: https://linktr.ee/nearshorecafepodcast
SHOW NOTES 🔗
TRANSCRIPT 🔗
00:00:12.720 --> 00:00:16.960
Welcome to another episode of the Nearshore Cafe Podcast.
00:00:17.120 --> 00:00:18.879
I'm your host, Brian Sampson.
00:00:19.039 --> 00:00:22.480
On this podcast, we talk about all things near shoring.
00:00:22.559 --> 00:00:30.719
And one of the most exciting things is when we talk to engineers doing real work like we're going to on this show today.
00:00:30.879 --> 00:00:36.640
Before I introduce our guest, let me thank our sponsor, Plug Technologies, P-L-U-G-G.
00:00:37.600 --> 00:00:41.039
Great way to connect talent from all over Latin America to growing U.S.
00:00:41.200 --> 00:00:41.679
companies.
00:00:41.920 --> 00:00:45.759
Victor Fernandez, we are so happy to have you.
00:00:46.000 --> 00:00:49.200
A real life, amazing engineer based in Brazil.
00:00:49.359 --> 00:00:50.079
Thanks so much for joining.
00:00:53.359 --> 00:01:03.359
So tell us just a little bit about what you're doing today, just a little bit, and then we'll rewind the clock and go through your career journey.
00:01:03.679 --> 00:01:04.480
What are you doing today?
00:01:04.640 --> 00:01:06.239
I'm a data platform engineer.
00:01:06.400 --> 00:01:10.079
I've been doing data platform engineering for the last six years.
00:01:10.239 --> 00:01:14.000
I work helping other teams doing their stuff.
00:01:14.079 --> 00:01:18.560
I'm I'm working developing new features for the engineers.
00:01:18.799 --> 00:01:22.319
So I'm a I'm a computer engineer.
00:01:22.400 --> 00:01:23.519
I have a bachelor in there.
00:01:25.280 --> 00:01:28.879
I think that's a good good endpoint for us to build into.
00:01:29.760 --> 00:01:32.640
Well, maybe let's just start off with the basics.
00:01:32.719 --> 00:01:33.519
Where did you grow up?
00:01:33.920 --> 00:01:38.640
I grew up here uh in Fortaleza, I I was not born here.
00:01:38.719 --> 00:01:40.079
I was born in South Brazil.
00:01:40.159 --> 00:01:44.000
Yeah, I'm in northeast Brazil, far away from I I was born.
00:01:44.159 --> 00:01:52.159
Um here's a really nice city to to live because we have a stable uh weather, but it's sunny all over the year.
00:01:52.400 --> 00:01:54.719
So yeah, I I grew up here.
00:01:55.200 --> 00:02:00.879
I've already traveled for other uh places, but I like to be here.
00:02:00.959 --> 00:02:02.400
I like to have grown here.
00:02:02.640 --> 00:02:02.879
Yeah.
00:02:03.120 --> 00:02:13.120
And you know, one of the things I think is really interesting about Brazil is it has had a lot of different cultures and immigrants, you know, come from all over the world.
00:02:13.360 --> 00:02:18.240
Um, you know, huge Japanese population and Sao Paulo and so on and so forth.
00:02:18.400 --> 00:02:20.879
Um were your were your parents born in Brazil?
00:02:21.120 --> 00:02:22.560
They were born in Brazil.
00:02:22.800 --> 00:02:29.919
My grandparents, uh they and grandgrandparents, they came from other from came from Italy.
00:02:30.080 --> 00:02:34.319
I've not met him, but I know that my family came from immigrants.
00:02:34.560 --> 00:02:36.080
Yeah, amazing, amazing.
00:02:36.240 --> 00:02:47.680
Yeah, I think um uh Brazil is is really famous for that as a melting pot of really, really, you know, amazing backgrounds that have built uh an amazing country.
00:02:47.919 --> 00:02:50.560
Now, were you always uh a technical guy?
00:02:50.800 --> 00:02:56.639
Were you always kind of tinkering and putting stuff yes, yes, I was thinking about that the other day.
00:02:56.800 --> 00:03:04.719
Uh I was the guy that break the stuff to see how they work, but I was never uh able to put it back uh to work again.
00:03:04.879 --> 00:03:06.560
So, yes, I grew up like this.
00:03:06.639 --> 00:03:08.960
Uh I've I've always been curious.
00:03:09.120 --> 00:03:14.800
Um, and I remember when my brother bought our first computer, I got fascinated about that.
00:03:15.120 --> 00:03:18.080
I think since I was five or six, something like that.
00:03:18.319 --> 00:03:21.199
I was always fascinated about computers and how it works.
00:03:21.360 --> 00:03:30.879
My brother used to scripting a little, now he's a lawyer, but I think he introduced me in some way of the about like the engineering stuff, computer engineering stuff.
00:03:31.039 --> 00:03:34.080
So I really like that since I was a kid.
00:03:34.319 --> 00:03:37.759
Do you remember um the model of your very first computer?
00:03:38.159 --> 00:03:40.479
Phase six, two, something like that.
00:03:40.800 --> 00:03:41.360
A hundred.
00:03:41.840 --> 00:03:43.599
But look, I was five or six.
00:03:43.759 --> 00:03:47.439
My brother was 13, he was seven years old older than me.
00:03:47.759 --> 00:03:50.960
So I barely remember the the model.
00:03:51.360 --> 00:03:57.039
Yeah, but you uh I mean, um big big moment, I think, in your life, you know, probably is you.
00:03:57.280 --> 00:03:57.840
Yeah, totally.
00:03:57.919 --> 00:04:10.639
I remember when 206 megabytes of RAM was too much, and I couldn't open like two browser screens, and when I upgraded that I was able to run some stuff.
00:04:10.800 --> 00:04:13.039
I was, oh my god, that's perfect today.
00:04:13.199 --> 00:04:13.919
There's nothing.
00:04:14.000 --> 00:04:15.840
So it's funny to think about this.
00:04:16.079 --> 00:04:16.720
That's so funny.
00:04:17.040 --> 00:04:24.720
Growing up in your in your neighborhood, did you meet other other kids that also shared an interest in computers and coding?
00:04:24.959 --> 00:04:27.040
Yeah, mainly from school.
00:04:27.279 --> 00:04:35.360
Uh I was I was like uh I wasn't new a lot of people, but I I got some closer friends and not some it was a fewer friends.
00:04:35.439 --> 00:04:41.759
Uh but they all used to like to play games in computers, like RPGs and board games.
00:04:44.959 --> 00:04:48.160
Like computer stuff, like to do as well.
00:04:48.399 --> 00:04:53.680
So yeah, I I grew up with this kind of people, which is I really enjoyed.
00:04:54.000 --> 00:04:54.319
Yeah.
00:04:54.560 --> 00:04:59.120
And what what were some of the um early computer games that you used to play with your friends?
00:04:59.600 --> 00:05:00.160
Tibia.
00:05:00.399 --> 00:05:05.600
I love I played a lot of tibia, Ragnarok, Gunbound, Need for Speed Underground.
00:05:05.680 --> 00:05:10.639
I remember when the first time I could turn in my car, oh my god, that was amazing.
00:05:10.800 --> 00:05:13.439
I have a lot of great experience in remembering.
00:05:13.600 --> 00:05:15.519
I used to go to a friend's house.
00:05:15.600 --> 00:05:20.480
I at the time I was I had I didn't I didn't get uh cable internet.
00:05:20.800 --> 00:05:25.600
I got after midnight after and a Friday and Saturdays after two.
00:05:25.920 --> 00:05:31.279
I don't know if where you live was like that, but here uh I had that kind of problem.
00:05:31.680 --> 00:05:35.519
My my I remember my my friend having a good internet.
00:05:36.000 --> 00:05:38.720
We played gumball for 24 hours free.
00:05:39.120 --> 00:05:41.040
Oh my god, that was amazing.
00:05:41.360 --> 00:05:47.759
I read live stream in front of the computer, just stopping to go had good times in my childhood.
00:05:48.000 --> 00:05:48.480
That's amazing.
00:05:48.639 --> 00:05:50.240
And then how about uh English?
00:05:50.399 --> 00:05:53.279
When did you first start to to practice that and learn that?
00:05:53.600 --> 00:06:08.240
I think with games, like for sure, because I used to play Pokemon on the Game Boy, and I like you I know today that we have to read stuff to understand what to do in the Pokemon, but I wasn't able to understand what it it was saying.
00:06:08.480 --> 00:06:16.399
So I I remember trying to go to the dictionary and see the words, what they were meaning, and I think that it helped me a lot.
00:06:17.040 --> 00:06:26.720
At the time there was not not a lot of translations to Portuguese for the game, so I had to learn what everything was saying, and I think that helped me a lot.
00:06:26.959 --> 00:06:34.000
Then I went to like when I was 15, I did a course uh English, so which helped me a lot.
00:06:34.079 --> 00:06:37.920
In the school, we had also classes, English classes.
00:06:38.079 --> 00:06:43.040
So putting all this together, I think it made a good base for what I know today.
00:06:43.360 --> 00:07:01.920
I think what you brought up is really interesting because you know, we um, you know, growing up, like we all kind of take a language in high school, secondary school, but you had a you had a real reason to do it, and that was too, you know, I remember like totally, totally.
00:07:02.000 --> 00:07:08.160
And I used to play one game called Bristol Tao, and the best updates came from the Korean server.
00:07:08.319 --> 00:07:10.800
I tried to play uh to learn Korean.
00:07:11.040 --> 00:07:16.480
Today I know the how to speak, but I don't know what what I'm reading, what I'm saying.
00:07:16.639 --> 00:07:22.399
I just know how to pronounce the the symbols, but I'm trying to learn that the Net was hard.
00:07:22.800 --> 00:07:23.920
That's so fun.
00:07:24.319 --> 00:07:28.560
Uh okay, so did you go right into platform engineering?
00:07:28.639 --> 00:07:33.600
So you studied computer science and straight away, or tell us more about your career.
00:07:33.920 --> 00:07:42.720
No, no, not and that's why because when I was in the graduation, this data world was not a thing.
00:07:42.959 --> 00:07:52.959
It was still like um this data is a new uh oil, is was kind of uh something that we used to hurt.
00:07:53.279 --> 00:08:04.240
But I I I grew up, like I graduated computer engineer, but my first work, and I worked for that for like seven and a half years, was like an automation engineer.
00:08:04.480 --> 00:08:25.839
I used to work in power plants, factories, commercial buildings, and I what my work there was to automate some machines, some machinery, and adapting that machinery to safety standards and creating supervisory systems for the for whatever I was automating.
00:08:26.000 --> 00:08:29.279
We had a team there, and it's a company needed my pipelines.
00:08:29.600 --> 00:08:40.399
And I worked seven and a half years for this company, so I did not start it as a data engineer, but it crossed my path uh in 2021.
00:08:40.720 --> 00:08:41.600
Okay, okay.
00:08:41.840 --> 00:08:48.000
And then, you know, as an automation engineer, that's interesting, you know, working in power plants and things like that.
00:08:48.159 --> 00:08:55.519
Were you were you like interviewing users and trying to understand the process first before you automated it?
00:08:55.759 --> 00:08:56.240
Yeah, yeah.
00:08:56.320 --> 00:09:02.399
We had to understand what was going on there, and it was always something different, which is kind of nice, right?
00:09:02.559 --> 00:09:10.240
Because you we go from power plants to steel factories, like I said, to sewering uh sewer treatment plants also.
00:09:10.399 --> 00:09:12.480
So we always learn something new.
00:09:12.639 --> 00:09:14.799
This is a pro of the job, right?
00:09:14.879 --> 00:09:17.120
But there was a lot of cons of that job as well.
00:09:17.360 --> 00:09:18.159
Yeah, yeah.
00:09:18.399 --> 00:09:21.120
What kind of uh languages were you programming in?
00:09:21.440 --> 00:09:22.080
Today.
00:09:22.320 --> 00:09:27.600
I program when you were in the the autom the original automation seven years, yeah.
00:09:27.840 --> 00:09:33.919
We used to program because we program not a usual computer like you imagine, maybe.
00:09:34.080 --> 00:09:35.840
We used to program PLCs.
00:09:36.000 --> 00:09:40.960
It's a programmable logical controller with a kind of industrial computer.
00:09:41.120 --> 00:09:44.240
So there's a specific language for this kind of computer.
00:09:44.559 --> 00:09:45.679
Two of three languages.
00:09:45.840 --> 00:09:49.759
I used to to program instructional lists, yeah.
00:09:50.000 --> 00:09:57.919
And for but for the supervisory system, which is Kata system, uh, we used to code in VB script, basically.
00:09:58.159 --> 00:09:59.519
Okay, okay, great.
00:09:59.679 --> 00:10:02.960
And then 2021, uh, we're right in the middle of COVID.
00:10:03.360 --> 00:10:03.679
Yeah.
00:10:04.320 --> 00:10:04.559
Yeah.
00:10:04.720 --> 00:10:09.360
Tell us uh this shift into um platform engineering.
00:10:09.440 --> 00:10:10.159
How did that happen?
00:10:10.240 --> 00:10:12.960
And what were what were some of the first uh projects?
00:10:13.120 --> 00:10:13.440
Yeah.
00:10:13.600 --> 00:10:18.080
Um it was kind of I was not expecting a call from my friend.
00:10:18.159 --> 00:10:20.000
He he lives in Sao Paulo.
00:10:20.080 --> 00:10:22.799
I I we graduated together today.
00:10:22.879 --> 00:10:28.879
Uh like uh luckily he's from my family today because some of our relatives got married.
00:10:29.279 --> 00:10:31.120
So we're from the same family.
00:10:31.279 --> 00:10:36.159
But he called me saying, I have a friend which is looking for someone.
00:10:36.320 --> 00:10:40.080
I think that uh we worked together in the past and he liked the way I worked.
00:10:40.320 --> 00:10:49.360
So he said, uh, I know that you're not able to move to Sao Paulo, and usually the jobs that we have here you have to move.
00:10:49.440 --> 00:10:55.120
But this one is totally a work from home, and they were approaching the work from anywhere.
00:10:55.279 --> 00:10:57.840
So I think this is a good opportunity for you.
00:10:57.919 --> 00:11:01.440
And I said, of course, let's go, let's let's try it out.
00:11:01.600 --> 00:11:09.440
And I was not at first, I was even not aware of what was the data platform, and I was not into this data world.
00:11:09.600 --> 00:11:17.200
I was more of a data producer sending data to the cloud and that stuff instead of all the governance side and consuming that data.
00:11:17.440 --> 00:11:21.679
So that's how this data engineering came to my path.
00:11:21.919 --> 00:11:22.240
Yeah.
00:11:22.480 --> 00:11:26.720
Tell us about um uh just some of like the very first projects.
00:11:26.879 --> 00:11:29.840
Like what were you trying to make sense of?
00:11:30.080 --> 00:11:45.840
Yeah, I remember first project, and I was lucky to get this project at my first experience, it was building a ingestion engine for like to bring data from databases, APIs, and different sources to a data lake.
00:11:46.000 --> 00:11:52.960
At that time, we didn't like I didn't have a lot of experience on this data consumption part.
00:11:53.200 --> 00:11:54.399
So that's what I did.
00:11:54.559 --> 00:12:11.840
Like I created this ingestion engine purely in Python, and I got uh like a great experience with my teammates because they were like extraordinary people and taught me a lot about governance, taught me a lot about software engineering as well.
00:12:12.159 --> 00:12:15.840
So I was blessed by this experience for sure.
00:12:16.080 --> 00:12:17.360
Yeah, that's amazing.
00:12:17.519 --> 00:12:20.720
And uh tell us more about the project that you're on today.
00:12:20.879 --> 00:12:21.519
What are you doing?
00:12:21.759 --> 00:12:33.759
Yeah, I work a lot of with CI CD pipelines and automating stuff for the data engineers to deliver faster and with a good governance in the background.
00:12:33.840 --> 00:12:52.159
Um, data platform team is uh is a lot of working with people for people, and I like this position because our clients they are not directly the clients of the company, they are engineers from the company, so we kind of create some bounds, right?
00:12:52.240 --> 00:12:54.320
Yeah, and I like to work with people.
00:12:54.399 --> 00:13:06.799
I like to say, can I can you have a quick call and hear the people, uh the person, you know, and I think that I like this kind of scenery with people, this warm way of working.
00:13:07.120 --> 00:13:16.080
Yeah, so we I create I basically create automations and develop uh new services for internal portal that we have.
00:13:16.240 --> 00:13:21.440
Everything usually I I do with Python and uh a lot of scripting as well.
00:13:21.679 --> 00:13:22.000
Yeah.
00:13:22.159 --> 00:13:27.200
And the other engineers that you're working with and and building for, where are they all based?
00:13:28.000 --> 00:13:32.159
They are based in the US or India, which is pretty common there.
00:13:32.320 --> 00:13:38.159
I work with a guy from Sri Lanka, and he's like, imagine the time zone difference that we have.
00:13:38.320 --> 00:13:47.759
I'm working 10 hours and a half from uh from the state that the company's based, which is eight hours and a half from where I'm living now.
00:13:48.159 --> 00:13:54.799
When I it's like it's morning for me and almost afternoon from him, you know, almost evening.
00:13:55.039 --> 00:14:08.879
So it's I say sometimes in the Friday, I tell him, dude, go away, go take a rest, because it's late night for you, 11 in the night, come on, just stop working, but it's it's on the theme.
00:14:09.600 --> 00:14:10.080
Yeah.
00:14:10.399 --> 00:14:13.679
How about when you're working with uh people in the States?
00:14:13.759 --> 00:14:21.440
Tell us about you know the cultural similarities, the cultural differences, how you all kind of you know put it together.
00:14:21.679 --> 00:14:36.960
That's funny you asked me that because when I first started working for a US company, it got me by surprise how the things were, how the people talk to each other and how the people behave in the meetings.
00:14:37.120 --> 00:14:43.519
I was used to something more warm, approachable, more informal way of talking with each other.
00:14:43.759 --> 00:14:57.039
Usually in Brazil, when you join a meeting at the like the first 30 seconds, people are already talking and asking how was your weekend, what you did, how your family, and warm way of talking, right?
00:14:57.200 --> 00:15:05.279
But when I started in the US company, like I joined the first daily human talk, and there was a silence, and I was what is happening here?
00:15:05.600 --> 00:15:07.279
Am I not hearing the people?
00:15:07.519 --> 00:15:08.320
Are they talking?
00:15:08.480 --> 00:15:11.600
So and they just thought, okay, let's go, let's do the daily.
00:15:11.759 --> 00:15:15.039
And it, you know, I felt it, but it shocked me, you know, you know.
00:15:15.279 --> 00:15:15.519
Yeah.
00:15:16.240 --> 00:15:22.879
Today I am totally comfortable with trying to have this this prior conversations with the the team.
00:15:22.960 --> 00:15:25.759
I know the team better than I used to.
00:15:25.919 --> 00:15:28.799
So I have friends there now, which which is good.
00:15:28.960 --> 00:15:35.600
We have a good time talking with each other, but you know, that got me by surprise how things work.
00:15:35.759 --> 00:15:36.080
Yeah.
00:15:36.320 --> 00:15:41.120
Well, hopefully you're able to maybe bring some of the warmth of the Brazilian culture.
00:15:44.639 --> 00:15:44.960
Yeah.
00:15:45.120 --> 00:15:45.440
Yeah.
00:15:45.600 --> 00:15:46.000
Yeah.
00:15:46.240 --> 00:16:12.399
And um uh you know, maybe um you could talk a little bit more about just like Brazilian culture for those who've never had a chance to visit, you know, what what should people know if they, you know, they're say from the States and they visit Brazil or they're from the States and they um start working with an engineer from Brazil, what are some things that they should be aware of or or or ready for?
00:16:12.639 --> 00:16:14.799
Yeah, I think that we like to talk.
00:16:14.960 --> 00:16:17.440
We like to have good conversation and that stuff.
00:16:17.600 --> 00:16:21.840
But it also depends where the because Brazil is huge, right?
00:16:22.000 --> 00:16:24.639
We have different cultures from all over the Brazil.
00:16:24.799 --> 00:16:29.919
So people from the south, they're more self-contained, let's let's say that way.
00:16:30.000 --> 00:16:37.919
But here in Northeast, we are known by our irreverent way of talking and do some jokes with another.
00:16:38.159 --> 00:16:43.519
So kind of depends where you're hiring the people from uh and where you're visiting.
00:16:43.679 --> 00:16:47.600
So if you come to Northeast or North, you have a great time.
00:16:48.879 --> 00:16:52.080
We have a great time, we have great landscapes, we have great people.
00:16:52.320 --> 00:16:55.279
I think that Brazilians are very receptive.
00:16:55.600 --> 00:17:07.359
So you will not like uh regret coming to Brazil, having some time here, or even hiring someone from here because I have an idea that we are hardworking.
00:17:07.759 --> 00:17:22.559
So when you have an opportunity to work speaking another language or knowing someone different from from our daily lives, we kind of put our hearts hearts on the job and put our hearts on what we are doing.
00:17:22.720 --> 00:17:24.480
So that's that's the feeling that I have.
00:17:24.559 --> 00:17:29.359
That's the way how I try to to behave daily and how I feel.
00:17:29.680 --> 00:17:34.079
Yeah, a lot of uh passion for everything, business, family.
00:17:34.240 --> 00:17:34.480
Yeah.
00:17:34.720 --> 00:17:36.079
Yeah, yeah, totally.
00:17:36.319 --> 00:17:36.640
Yeah.
00:17:36.880 --> 00:17:42.400
And sometimes there are there are myths, you know, like things that we hear, but we don't know if they're true.
00:17:42.559 --> 00:17:49.039
Sometimes there are myths about working with Brazilians and they're always two hours late or three hours late.
00:17:49.200 --> 00:17:52.880
Can you can you confirm or deny your experience?
00:17:53.279 --> 00:17:55.359
Yeah, I I this is a myth, I think.
00:17:55.519 --> 00:18:00.079
I think uh it depends a lot of the person itself.
00:18:00.400 --> 00:18:10.880
Um but I can say some some people usually get uh I mean look for for some stuff, but for jobs is different.
00:18:11.039 --> 00:18:14.400
If I go, uh it's common sometimes here where I live.
00:18:14.640 --> 00:18:24.319
If you schedule something to someone and like be here at 7, sometimes they they show up 7 at 30 or 8 sometimes, which is not good.
00:18:24.559 --> 00:18:28.880
Some people just got used to it, some people just get mad with that.
00:18:29.039 --> 00:18:30.640
Uh, but it's different with jobs.
00:18:30.799 --> 00:18:32.319
Here we're kind of punctual.
00:18:32.480 --> 00:18:32.720
Yeah.
00:18:32.960 --> 00:18:39.440
Victor, how is AI changing the way you changing the way you work, the way you approach your job?
00:18:39.599 --> 00:18:41.279
Oh, yeah, good question.
00:18:41.440 --> 00:18:49.920
And what I can say is AI is saving a lot of time, but when used by the the correct people, right?
00:18:50.160 --> 00:19:21.920
So in my job, when I have a well-scoped work, so AI helped a lot on delivering faster, um, but it also brings some responsibility of what you were doing actually and what you're putting in production, because sometimes it can provide you some code or some advice that may not fit well with the company, you know, with the system that you're building or where it is already running in production.
00:19:22.240 --> 00:19:33.200
So you have to basically judge a lot when using AI, because sometimes the advice is not good enough or not well suitable for the problem.
00:19:33.440 --> 00:19:45.039
It is good if you're building something from the from the scratch, but adding some stuff on something that is already running and producing value, you have to judge a lot of what AI is doing.
00:19:45.200 --> 00:19:54.640
So it helps a lot, and all the companies are pushing the employees to use AI to be faster, but you have to be careful.
00:19:54.880 --> 00:19:56.319
Yeah, yeah, good advice.
00:19:56.559 --> 00:20:05.680
As we start to uh wind down the show, I'd love for you to tell us more about your current city and you know play tour guide for a minute.
00:20:05.920 --> 00:20:10.720
If anybody listening is like, oh my gosh, I I have to visit, what should they do?
00:20:10.960 --> 00:20:12.160
What should they eat?
00:20:12.319 --> 00:20:13.119
What should they know?
00:20:13.680 --> 00:20:16.880
Yeah, here we have a lot of options to go out.
00:20:16.960 --> 00:20:22.160
We have a pretty active nightlife here because I live in a touristic city.
00:20:22.400 --> 00:20:26.880
Fortaleza is the sixth most populated city in Brazil.
00:20:26.960 --> 00:20:28.160
So it's a huge city.
00:20:28.319 --> 00:20:33.039
We have great landscapes, we uh we are next to a lot of beaches.
00:20:33.279 --> 00:20:39.279
So from where I live, I live like 20 or 30 minutes away for three different beaches.
00:20:39.440 --> 00:20:43.200
So we have a lot of things to do to see.
00:20:43.440 --> 00:20:48.799
We have Japanese food, Italian food, Arab food, all kinds of foods here.
00:20:49.039 --> 00:20:55.359
So you never run out of options of where to go during the night or have a great lunch.
00:20:55.519 --> 00:21:02.799
Yeah, so like from hours from here, you can be in another another state with also a lot of good beaches.
00:21:03.039 --> 00:21:09.920
So uh if you have any opportunity, come to Fortaleza and and have a great time here.
00:21:10.079 --> 00:21:15.440
We're always always like acceptable with new people and just come visit us.
00:21:15.680 --> 00:21:26.319
It sounds like a warm place in the weather and and yeah, yeah, we are kind of 84 Fahrenheit, which is 30 32 Celsius all over the year.
00:21:26.480 --> 00:21:28.799
Even if it's rainy here, it's rainy and hot.
00:21:29.039 --> 00:21:34.240
So you you don't have to bring your jacket and all that, all that stuff.
00:21:34.640 --> 00:21:35.440
That's great.
00:21:35.680 --> 00:21:38.319
Well, Victor, this has been uh such a pleasure.
00:21:38.480 --> 00:21:47.200
It's just really interesting how you've gone from automation and into uh data engineering, platform engineering, and how you're incorporating AI.
00:21:47.359 --> 00:21:50.400
I've learned a lot, and I think our listeners have too.
00:21:50.480 --> 00:21:51.359
So thank you so much.
00:21:51.680 --> 00:21:54.319
Thank you so much, Brian, for this opportunity.
00:21:54.640 --> 00:21:59.279
And always that you need, you can join me again and can have a great talk here today.
00:21:59.759 --> 00:22:00.319
Absolutely.
00:22:00.480 --> 00:22:05.119
Well, let me thank our sponsor again, Plug Technologies, P L U G G dot tech.
00:22:05.519 --> 00:22:09.200
Great way to connect talent from all over Latin America to growing U.S.
00:22:09.359 --> 00:22:09.759
companies.
00:22:09.920 --> 00:22:14.079
I'm your host, Brian Sampson, and this is the Nearshore Cafe Podcast.
00:22:14.240 --> 00:22:15.039
We'll see you again soon.