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I built you a free tool that matches you to open data jobs! I built it using a low-code analytics tool called KNIME. Learn how I built it & how you can build your own!

👔 Try the Resume to Job Match App: https://apps.hub.knime.com/d/AI_Job_Search~data-app:c0cff571-721d-4cd2-aca7-5f19505d7537/run?authToken=cVZoMmVVZmF3RkM4eG9HdXVDdWltWTlZbFJDdzRCTjc0TXBLaFIzY3JYSTpmT3FYZ3VWZ19OTHgwZExxMGZ1Ty1XMUxySFF0QmdYRXBOWllaSVVQRnZmbnRHdGlnWlJ1cS1lM2hlQk0tM3k0TE0taHk3b0ZyQTh2S0t2YWV4QkREdw==

💻 Download KNIME: https://www.knime.com/start?utm_source=youtube&utm_medium=influencer&utm_term=avery_smith&utm_content=video&utm_campaign=kapsquad

📈 Download the KNIME Workflow: https://hub.knime.com/knime/spaces/Data%20Apps/AI_Job_Search~IkoQH5UBhidlxnEt/current-state?utm_source=youtube&utm_medium=influencer&utm_term=avery_smith&utm_content=video&utm_campaign=kapsquad

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⌚ TIMESTAMPS

0:00 - Avery's notes about episode (audio only)

7:33 - Use this free tool to find data jobs

09:36 - What is low-code analytics & why is it important?

13:20 - How I built this tool (& you can too)

16:47 - The future of low-code data tools

🔗 CONNECT WITH AVERY

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💻 Website

Mentioned in this episode:

🎉 The 2026 Cohort of The Data Analytics Accelerator 🎉

Ready to land your data job in 2025? We're starting the 2026 Cohort on January 5th. This bootcamp is everything you need to land your first data job. Check out our New Year's sale & bonuses: https://datacareerjumpstart.com/daa

https://www.datacareerjumpstart.com/daa

Speaker: 00:00:00

Hey, podcast listeners, Avery Smith here.

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: 00:00:02

I've been wanting to tell you guys about

this for a while, but like you've probably

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: 00:00:05

noticed that I've started to make a lot

of my podcast episodes more video heavy,

4

: 00:00:11

and I wish that I could put the videos

on podcast warm, but that's just not how

5

: 00:00:16

it's really set up to work right now.

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: 00:00:18

Um, so one thing I was thinking

about is how can I make these

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: 00:00:21

video more video-centric episodes?

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: 00:00:23

Better for you guys and the listeners.

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: 00:00:25

Right.

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: 00:00:26

First off, this one that you're gonna

about to listen to, I don't think

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: 00:00:28

it's super important to have video.

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: 00:00:31

Um, I do talk about like a

little bit of a workflow.

13

: 00:00:34

Um, but actually if you go to the

description and you click on the

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: 00:00:38

link, that will take you to the

workflow that will actually show

15

: 00:00:41

you a picture of the workflow.

16

: 00:00:42

So you could actually have

that open if you wanted to.

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: 00:00:44

Um, otherwise I like try to explain it

so you don't need the visual reference.

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: 00:00:48

Um, but I've been thinking about

like these more video centric

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: 00:00:52

episodes about how can I make it

more special and interesting to

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: 00:00:55

you guys as podcast listeners.

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: 00:00:58

And one of the ideas I had, I had was

like, kind of doing this more free

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: 00:01:01

flowing casual intro to the episodes.

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: 00:01:05

That you're listening to right

now, that kinda gives you a little

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: 00:01:07

bit more, uh, behind the scenes.

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: 00:01:10

And maybe is on a lot of time when you're

making a YouTube video, you have to keep

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: 00:01:13

it pretty short and brief because people's

attention spans are short, including mine.

27

: 00:01:18

Uh, but when you're doing a podcast, you

can like listen a little bit longer, um,

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: 00:01:22

and get a little bit more of the details.

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: 00:01:23

So I was like, maybe I can kind of

do a little preamble to the, uh.

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: 00:01:27

The actual episode and give

you guys a little bit more

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: 00:01:30

of an audio only experience.

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: 00:01:32

So, um, let me know in the Spotify

comments if you like this or not, or

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: 00:01:37

you can send me an email too and tell

me your thoughts on the podcast, um,

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: 00:01:41

and maybe how we could do better with

these more video centric episodes.

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: 00:01:45

You can send me an email, my email's

avery@datacareerjumpstart.com

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: 00:01:49

and you can just put in the

headline like podcast feedback.

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: 00:01:52

I'd love to hear from you because it's

something I'm actually thinking about

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: 00:01:54

is how do we make the audio podcast.

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: 00:01:57

Continue to be the number one

data podcast on Spotify, which you

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: 00:02:00

guys, thanks to you guys we are.

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: 00:02:02

Which is super cool.

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: 00:02:03

Um, so this episode it's about a

tool I made using a no code slash low

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: 00:02:08

code, uh, data analytics platform.

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: 00:02:11

And, um, I explain how I

built it, what the tool is.

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: 00:02:14

It, it basically, you upload your

resume and it matches you with jobs.

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: 00:02:17

And I explain kind of why I think no code

and low code tools are, are important.

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: 00:02:23

But I just wanna tell you guys, I do think

that these tools are really important.

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: 00:02:26

I think they are going to be

used more in the future and they

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: 00:02:30

actually make data analysis easier.

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: 00:02:32

So if you're brand new to data analytics

these tools are, are pretty cool.

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: 00:02:35

Um, now they aren't used like all the time

in industry, but they're becoming more

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: 00:02:40

popular and I think they'll be, continue

to become more popular down the road.

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: 00:02:45

Um, if you're unfamiliar with them,

just think instead of like writing

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: 00:02:47

code, like SQL or Python or doing

a spreadsheet like Excel or like a

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: 00:02:53

dashboard like Power BI or Tableau.

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: 00:02:56

You have like a blank canvas.

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: 00:02:58

And on that canvas, you, you can

kind of think of it almost like

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: 00:03:02

a factory, like a data factory.

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: 00:03:05

Um, where, you know, data starts on

like the left hand side, and then

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: 00:03:09

your analysis is gonna be on the, the

output of the analysis is gonna be

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on the right hand side and in between

there's like all these like magic

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: 00:03:17

machines that will do certain things.

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: 00:03:19

So like, you'll have a data

cleaning machine, right?

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: 00:03:21

You put the data through the left

hand side through the data cleaning

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: 00:03:24

machine, and out comes clean data.

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: 00:03:26

And then you could have like a, a

bar chart machine where you feed

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: 00:03:29

in the data and out the right

hand side comes a bar chart.

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: 00:03:32

Um, and you could have all

these different little machines.

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: 00:03:34

These are called nodes in the tool

I was using, which is called nine.

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: 00:03:38

Um, these nodes basically perform one

little bite sized data operation for you.

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So it could be, you know,

cleaning all the dates.

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: 00:03:46

It could be cleaning you

know, the null values.

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It could be doing regression.

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: 00:03:52

It could be, you know, any sort of

data thing you could be doing can

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be done in this no-code platform.

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So, I think it's really cool.

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: 00:03:59

Nym this, this pon, this

episode is sponsored by Nime.

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: 00:04:02

They actually reached out to me and

I was super excited when they reached

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: 00:04:05

out to me, and I, I talk about this in

the episode, but Trevor, uh, Maxwell,

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: 00:04:09

who you may have listened to his

interview where he went from no college

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: 00:04:12

degree printer technician to landing

a totally remote data analyst job.

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: 00:04:17

He actually uses Nime a decent

amount at work, which is super cool.

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: 00:04:20

And he's, he's told me how,

how Nims he really enjoys Nime.

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: 00:04:24

And I was like, yeah, I think

low-code, no-code stuff is cool.

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: 00:04:27

We don't talk about any of that

inside of the bootcamp, inside

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: 00:04:29

of my accelerator program.

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: 00:04:31

Trevor, why don't you make a, um, and I,

I've hired Trevor now, if you didn't know.

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: 00:04:35

He's one of my coaches inside

of my accelerator program.

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: 00:04:38

Um, helps manage the students,

answers, questions, does some office

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: 00:04:41

hours, gives people like a little

bit of an insight of what it's

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like to actually be a data analyst.

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You know, working right now.

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: 00:04:48

And I had him create, uh, like a

half hour intro to Nime, uh, to our

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accelerator students, so that way if they

see it on job applications or if they

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: 00:04:56

see low-code, other no-code, low-code

platforms, they would know about it.

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So anyways, uh, big fan of Nime.

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: 00:05:02

I think it was really cool to have

them reach out and I was super happy to

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: 00:05:06

tell more people about the tool because

one, I think it's a cool tool and, uh,

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two, I think everyone should be aware

of how to do these types of low code.

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: 00:05:14

No-code analysis and what

the pros and the cons are.

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: 00:05:17

I talk a little about the pros.

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In the episode that we'll get

to here just in one second.

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I don't really talk too much of the cons.

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: 00:05:24

The, the trade off that a lot of people

will tell you is low-code, no-code tools

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: 00:05:29

make it easier to do data analysis.

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: 00:05:31

Like it's just easier to set up.

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: 00:05:33

It's actually easier to

do all the operations up.

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: 00:05:35

You know what you're trying to do.

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: 00:05:37

Um, but sometimes they come

at a sacrifice of like speed.

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: 00:05:42

So I know there are some data

engineers who would argue that like

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: 00:05:45

low code tools are inefficient and

they really slowed down things from

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a speed and memory perspective.

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I haven't really dealt with these

tools at scale, so I can't really

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: 00:05:56

speak to how big of a problem that is.

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: 00:05:59

So if you're currently employed as a

data professional or down the road when

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: 00:06:02

you, you know, land your first data

job and they're talking about this,

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that's, that's basically a trade off.

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: 00:06:06

You can, you can know is there, there,

there could be a trade off between how.

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Efficient the tools are

versus how easy it is.

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: 00:06:14

It's easier to set up, but it

might not be as like efficient

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: 00:06:17

from a data storage perspective.

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: 00:06:18

Now, I can't speak specifically

on Nime and like I've never worked

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: 00:06:21

with, with an a low-code tool

in my personal data per career.

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: 00:06:26

I'm pretty sure maybe I have a little bit.

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: 00:06:29

We have a little bit.

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: 00:06:30

But speed and efficiency

wasn't a big issue for us.

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: 00:06:33

So if you're in a place where speed

and efficiency are really, really,

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: 00:06:36

really, really, really important,

so like think about like automatic

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: 00:06:39

trading on the stock market, right?

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: 00:06:40

Even one second late, you can be

losing on, you know, a lot of money.

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: 00:06:44

So those situations, you know, you might

want to lose some of the infrastructure,

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: 00:06:48

the ease of like a low-code, no-code tool.

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: 00:06:51

Um, but if you're doing what I was kind

of doing like at ExxonMobil where the

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stuff you're predicting is months out.

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: 00:06:57

If it takes five extra minutes

to run, it's not a big deal.

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: 00:07:00

So anyways, that's a little bit of

some background on this episode.

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: 00:07:04

Tell me in the Spotify comments if

you kinda liked this preamble, uh, to

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: 00:07:08

the episode that's about to happen.

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: 00:07:09

And if you want more of it, um, once

again, when I talk about the workflow

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: 00:07:14

in the upcoming episode, go to the

show notes down below and uh, you can

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: 00:07:18

see a picture in the download nine.

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: 00:07:20

Uh, workflow Link and that

will show you the picture if

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: 00:07:22

you want to have the visual.

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: 00:07:24

Otherwise, I think I did an okay job

of giving you the visual without,

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: 00:07:27

without actually showing you the visual.

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: 00:07:29

Okay.

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: 00:07:29

Enough of my preamble.

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: 00:07:30

Let me know if you enjoyed this

and let's get into the episode.

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: 00:07:32

Okay.

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: 00:07:33

Speaker: Job hunting sucks right now.

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: 00:07:35

So in an effort to help you out,

I built a free tool that allows

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: 00:07:39

you to find specifically tailored

data jobs that are great fits for

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: 00:07:43

you based only on your resume.

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: 00:07:46

Seriously, just upload your resume

and boom, you got tailored job

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: 00:07:50

listings ready for you to apply.

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: 00:07:52

And I built this entire tool

using a data platform called nym.

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: 00:07:55

Which allows you to do data analytics

and data engineering with a visual

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: 00:07:59

interface, which makes the whole process

kind of feel like building data Legos.

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: 00:08:03

And they're actually the sponsor for

this episode, but more on them in a bit.

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: 00:08:07

So to test this tool out and to see

if it was any good, I asked one of

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: 00:08:10

my bootcamp students, you it, if

we could try it with her resume.

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: 00:08:13

So here's what we do, you use

the link in the show notes

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: 00:08:16

down below and you're going to.

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: 00:08:17

Upload your resume.

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: 00:08:18

So we'll go ahead and upload UITs

resume, then go for the job title

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: 00:08:22

you want in this case, data analyst.

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: 00:08:25

Next you're going to say your location

UITs is Charlotte, North Carolina.

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: 00:08:30

And finally, you're going to give a brief

summary of what your search goals are.

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: 00:08:33

So for her, it's going to land first.

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Data analyst job, you'll then press

next right here and wait a few

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: 00:08:40

seconds for it to finish the results.

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: 00:08:42

And bam, you get your own

personalized dashboard here.

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So on the left hand side, this is where

you have the top 10 job matches for you.

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Uh, and a little bit of

personalized tips right here.

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Then you have the job map here that lets

you see where the different jobs are

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and some basic information about them.

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So if you live in a certain

area, you can check it out there.

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And then on the right hand side.

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This is basically the top 10

jobs that it has for you with the

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descriptions, the location, the

company, as well as the salary.

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And of course, a link to apply.

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You'll need to hit control.

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: 00:09:12

Click to open that up.

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: 00:09:14

There's also a fun little histogram

up here that shows you kind of,

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: 00:09:16

uh, the salary distributions of

what you can kind of expect based

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off of the jobs it found for you.

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: 00:09:21

Pretty fun way to just find some new

jobs that you could possibly apply for.

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I have a link for you to try this tool

down in the description down below.

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: 00:09:28

So go try it out and let me know

what you think in the comments.

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: 00:09:31

I am of course, hoping it'll be

useful for you and help you find

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a few data jobs to apply for.

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But in the meantime, let me show you

how I built it and how you can actually

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: 00:09:39

build something quite similar, even

if you're not a data expert already.

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: 00:09:43

To start, I want to tell you about

why I chose to build this tool in

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nine, and you may have never heard of

nine before, and that's totally okay.

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: 00:09:51

I'll explain what it is and when you

might use it so that way if you ever

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: 00:09:54

see it on a job description, you don't

have to like panic and be like, oh my

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: 00:09:57

gosh, I have no idea what this thing

is, can I, I've never even heard of it.

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: 00:10:00

Right?

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: 00:10:00

Uh, let's explain what it's, so,

like I mentioned earlier, Nime is

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: 00:10:03

a data analytics and automation

platform, and it's honestly becoming

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: 00:10:07

a more popular choice of doing data

analytics and data engineering because

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: 00:10:11

one, it's free and open source and

we like free, we like open source.

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: 00:10:15

Who doesn't like that?

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: 00:10:15

Right?

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: 00:10:16

And number two, it makes data analytics

and data engineering honestly.

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: 00:10:19

Fun and easy using nine feels

like playing with data Legos.

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: 00:10:24

Instead of writing really long coding

scripts, you're actually pushing data

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: 00:10:27

through a visual workflow with data

building blocks like data Legos, and

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: 00:10:32

it's a lot easier to do complex data

manipulation because you're doing it with

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: 00:10:36

like little building blocks and Legos

instead of writing lots of code, which

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: 00:10:39

of course that makes it more fun and it

makes it a lot easier to do, especially

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: 00:10:43

if you're not even all that technical.

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: 00:10:44

You can do this stuff

and it's not too bad.

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: 00:10:46

Another reason I chose nine is it has

this awesome feature that makes it super,

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: 00:10:50

super, super easy to share data, web apps

like this resume one that I showed you

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: 00:10:55

earlier with just a couple clicks, and

that is insanely valuable, uh, because

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: 00:11:00

that's actually usually pretty hard to do.

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: 00:11:02

Let me explain why with a quick story.

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: 00:11:04

So when I was data scientist

at ExxonMobil, I was doing

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: 00:11:07

cool data science things.

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: 00:11:08

I was building cool data science projects,

and I like to think that these projects

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: 00:11:12

were technically difficult, right?

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: 00:11:13

We were doing cool things

where we were like.

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: 00:11:15

Predicting gasoline consumption

and trying to figure out what

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: 00:11:18

oils to buy around the world.

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: 00:11:19

And we were doing most of these projects

in Python, and it was like hundreds

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: 00:11:23

and hundreds of lines of Python code.

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: 00:11:26

We were using all sorts of different

Python libraries, some that you've heard

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: 00:11:29

of, some you've never heard of before.

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: 00:11:30

And we were using some decently

complicated machine learning algorithms.

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: 00:11:34

And so it wasn't easy.

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: 00:11:35

It was definitely like kind of hard.

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: 00:11:37

But despite all that.

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: 00:11:38

The hardest part might have

actually been when we actually

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: 00:11:41

finished writing all of the code.

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: 00:11:43

We're like, Hey, we built this really

cool tool, but how do we actually give

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: 00:11:46

it to end business user who doesn't know

how to use Python, doesn't have Python

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: 00:11:50

installed in their machine, doesn't

know anything about data, like how do

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: 00:11:53

we give it to a layman and be like, Hey,

we built this cool tool, now use it.

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: 00:11:57

Uh, and that was actually a

really hard question to solve.

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: 00:12:00

You could send them the script and

install Python on their computer, but

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: 00:12:03

that seems like overly complicated.

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: 00:12:05

And also, how do they actually run it?

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: 00:12:06

Do they just click run in like an id?

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: 00:12:08

It's, it's complicated.

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: 00:12:09

Do we just give it to a notebook?

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: 00:12:10

I don't know.

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: 00:12:10

That doesn't seem like we're

actually solving the problem.

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: 00:12:12

So, um, this is something that we

actually solved at Exxon most of the

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: 00:12:16

time by building pretty intensive.

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: 00:12:18

Web apps, but that required us to

write more Python codes, so we'd solve

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: 00:12:22

the problem with more Python code.

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: 00:12:24

And that took a ton of time and a ton

of resources because not only is it a

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: 00:12:28

lot of coding, but it's also a ton of

data engineering to give us like the

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: 00:12:31

access and like the CPUs and, uh, I don't

know, hosting it and all that stuff.

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: 00:12:35

It's, it's kinda over my pay grade, but it

was annoying and it would take forever and

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: 00:12:39

it would require a lot of people's help.

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: 00:12:40

Like I couldn't just do it by myself.

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: 00:12:42

At Exon.

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: 00:12:43

So with this tool with ny, it has like

this data app feature built right in,

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: 00:12:47

which makes it super easy to just hand

off to end business users like yourselves.

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: 00:12:51

So it's kinda like Tableau public.

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: 00:12:52

If you've ever used Tableau public

before, where you can just create

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: 00:12:55

a dashboard and share a link right

away, nys the exact same way where

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: 00:12:57

you can create these dashboards

or web apps and you have a link.

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: 00:13:00

You just copy that link and you can

literally send it to, uh, anyone you

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want to, and they can use your cool tool.

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So if you or your company are doing

complex data operations with lots

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of data manipulation, lots of data

visualization, and you wanna do it in

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like a simple, fun manner that makes it

easy to share and for easy for people to

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use, nine might be a really good choice.

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And that's when you'd want

to maybe check it out.

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So let me actually explain how you'd

use these data legos and how they work

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here by showing you how I built this

tool and how you could even rebuild

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it yourself or download the workflow

template and then just edit it and

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make some changes and make it your own.

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So basically what you need to know is this

workflow has three to four main parts,

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the resume part right here in yellow.

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The job part here in Orange.

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And then of course the dashboard part

here in red, which is kind of split

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into two with the AI part here, and then

the assembling the dashboard part here.

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Now each one of these little squares

that you see right here is called

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a node, and data flows in the node

from this direction or some sort

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of data transformation occurs.

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And then out the other side to future

nodes downstream as illustrated kind

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of with these arrows right here.

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Now, let's break down each one

of these sections one by one.

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The first part we have right here is

the resume portion, and this is just

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really a node right here, which basically

creates the landing page that you

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saw earlier with all those different

form questions to upload your resume

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and to tell them about yourselves.

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Now, usually building

webpage like this is.

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Pretty difficult to do, but seriously,

not super hard to do In a platform

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like nine where it's all low code,

drag and drop, no coding required.

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Next we have the job board section over

here, which is in orange, and the first

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thing we do here is we have our jobs API

note, which is actually just almost like

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a folder for a whole little mini work.

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Flow inside of the folder.

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Now you'll see here that we're using a

get request node, which just basically

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lets us call our job board API based

on the information that we gave it

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from that landing page, and you can

see some of the data inside of it.

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It's basically pulling the

titles, so on and so forth.

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Then the remaining nodes are

kind of just to clean and

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organized the job listing data.

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Job listing data obviously is

not super structured a lot of

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the time 'cause it's just words.

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It's not, numbers doesn't

fit like in a table, right?

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So this is just kind of us

organizing and cleaning that data.

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And then the remaining nodes inside this

section right here are essentially just

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doing more data cleanup, helping us create

the data that will feed into our LLM.

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We're also doing some data cleaning

down here with regular expressions where

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we're trying to pull out the salaries.

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Inside of the job descriptions and then

report that to you on the dashboard page.

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Now, all the data here basically

gets fed into this section right

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here, which is our AI section.

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The top nodes up here are where you're

going to input your own L-L-M-A-P-I

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keys, which is basically your password

to use some sort of tool like OpenAI

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inside of this app, and then of course

selecting what model you'd like to use.

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In this case, we are

choosing to use GPT-4 0.1,

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and then lastly, we have this

LLM prompter node down here at

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the bottom, which is essentially.

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Our message to chat GPT with

instructions on what to do.

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Now you'll notice with all these arrows,

it's getting our personal resume info,

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the job listing info, and all of the info

from the previous task passed into that.

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All of our information is then fed

into our final section right here,

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which is the dashboard node, and this

is the final page of the web app.

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This is where we have all the settings

to create the actual dashboard.

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Page.

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So to recap, in this workflow, we have

a form that first collects all of the

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user data, a data cleanup section that

extracts information from the resume,

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cleans it up, and then calls a job

board API, an AI section that creates

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custom tips for us based off of all

the previous information collected.

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And then lastly, our user-friendly

dashboards to display all

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that information to the user.

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And this is all built using nym.

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Data building Block Legos.

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So super fun to build

and super easy to do.

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So I hope you now see how it's

possible to do serious data analysis

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workflows using this type of platform.

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And I know a lot of companies are

using tools like Nine to simplify

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their lives, make it easy, and

create cool things like this.

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In fact, one of the alumnis.

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From my bootcamp, Trevor Maxwell,

use his name a decent amount at work.

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You can hear Trevor's full story of going

from printer technician to data analyst

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by clicking on the YouTube card here.

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Or I'll have a link to it

down below in the show note.

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And by the way, if you wanna customize

this workflow and make it your very

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own or make it better, 'cause I

definitely think it could be better.

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You totally can.

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So step one, you just wanna

download this example workflow.

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We'll have a link to it in

the description down below.

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Next, you'd want to download nine

and it's free to get started.

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We'll also have a link to that

down below in the description.

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And then lastly, you'd want to open

that downloaded workflow in nine.

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From here, you can edit any of the nodes

or add more nodes or take away things.

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You can literally do almost anything.

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Just remember, you'll need to enter

your own open AI API and job board API.

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And this is probably the

trickiest part since these are

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third party external tools.

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So that would be a little bit tricky.

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But other than that, you could

literally do so many things.

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You could increase the

number of jobs returned.

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You could add some analysis about

what skills are mentioned the most.

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Maybe you could even create some

sort of a scoring system, like

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there's so many different things

you could be doing with this guys.

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So go ahead, try it out.

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Overall, I hope you now one,

have a fun tool to test, to try

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to find different data jobs.

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Two, you understand how you

can actually analyze real data.

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With low code building blocks.

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Legos, right?

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It's a lot of fun.

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And three, I hope you know what

nine is, so you won't be scared

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when you see it on a job description

and you can be like, oh yeah, Avery

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talked about that in an episode.

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I'm hoping all of this is going

to help you on your data journey,

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and if you want more help on

your data journey, hit subscribe.

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I've got a lot more useful tips to share.