Exploring Job Market for Software Engineers

Introduction

In this article, we dive into the process of extracting and analyzing job data from LinkedIn, leveraging a combination of Python, Nu shell, and ChatGPT to streamline and enhance our workflow.

I’ll walk you through the steps I took to carry out my research, showing how you can use these techniques to explore job markets in different countries or even in other fields. By combining these tools and methods, you can gather and analyze data to gain valuable insights into any job market you're interested in.

Technologies overview

Python

Python was chosen for its versatile libraries, particularly linkedin_jobs_scraper and openai. These packages streamlined the scraping and processing of job data.

Nu Shell

Nu shell was experimented with to compare its functionality against the traditional bash stack. This experiment aimed to explore its potential benefits in handling and manipulating data.

ChatGPT

ChatGPT was employed to assist in the extraction of specific job features from the collected data, such as years of experience, degree requirements, tech stack, position levels, and core responsibilities.

Data extraction

To start some data is required. LinkedIn was the first website that came to my mind and there was ready to use Python package. I've copied example code, modified it a little and got ready to use script to get a JSON file with a list of job descriptions. Here it's source:

import json
import logging
import os
from threading import Lock

from dotenv import load_dotenv

# linkedin_jobs_scraper loads env statically
# So dotenv should be loaded before imports
load_dotenv()

from linkedin_jobs_scraper import LinkedinScraper
from linkedin_jobs_scraper.events import EventData, Events
from linkedin_jobs_scraper.filters import ExperienceLevelFilters, TypeFilters
from linkedin_jobs_scraper.query import Query, QueryFilters, QueryOptions

CHROMEDRIVER_PATH = os.environ["CHROMEDRIVER_PATH"]

RESULT_FILE_PATH = "result.json"
KEYWORDS = ("Python", "PHP", "Java", "Rust")
LOCATIONS = ("South Korea",)
TYPE_FILTERS = (TypeFilters.FULL_TIME,)
EXPERIENCE = (ExperienceLevelFilters.MID_SENIOR,)
LIMIT = 500

logging.basicConfig(level=logging.INFO)
log = logging.getLogger(__name__)


def main():
    result_lock = Lock()
    result = []

    def on_data(data: EventData):
        with result_lock:
            result.append(data._asdict())

        log.info(
            "[JOB]",
            data.title,
            data.company,
            len(data.description),
        )

    def on_error(error):
        log.error("[ERROR]", error)

    def on_end():
        log.info("Scraping finished")

        if not result:
            return

        with open(RESULT_FILE_PATH, "w") as f:
            json.dump(result, f)

    queries = [
        Query(
            query=keyword,
            options=QueryOptions(
                limit=LIMIT,
                locations=[*LOCATIONS],
                filters=QueryFilters(
                    type=[*TYPE_FILTERS],
                    experience=[*EXPERIENCE],
                ),
            ),
        )
        for keyword in KEYWORDS
    ]

    scraper = LinkedinScraper(
        chrome_executable_path=CHROMEDRIVER_PATH,
        headless=True,
        max_workers=len(queries),
        slow_mo=0.5,
        page_load_timeout=40,
    )

    scraper.on(Events.DATA, on_data)
    scraper.on(Events.ERROR, on_error)
    scraper.on(Events.END, on_end)

    scraper.run(queries)


if __name__ == "__main__":
    main()

To download chrome driver I've made the following bash script:

#!/usr/bin/env bash
stable_version=$(curl 'https://googlechromelabs.github.io/chrome-for-testing/LATEST_RELEASE_STABLE')
driver_url=$(curl 'https://googlechromelabs.github.io/chrome-for-testing/known-good-versions-with-downloads.json' \
    | jq -r ".versions[] | select(.version == \"${stable_version}\") | .downloads.chromedriver[0] | select(.platform == \"linux64\") | .url")
wget "$driver_url"
driver_zip_name=$(echo "$driver_url" | awk -F'/' '{print $NF}')
unzip "$driver_zip_name"
rm "$driver_zip_name"

And my .env file looks like that:

CHROMEDRIVER_PATH="chromedriver-linux64/chromedriver"
LI_AT_COOKIE=

linkedin_jobs_scraper serializes jobs to the following DTO:

class EventData(NamedTuple):
    query: str = ''
    location: str = ''
    job_id: str = ''
    job_index: int = -1  # Only for debug
    link: str = ''
    apply_link: str = ''
    title: str = ''
    company: str = ''
    company_link: str = ''
    company_img_link: str = ''
    place: str = ''
    description: str = ''
    description_html: str = ''
    date: str = ''
    insights: List[str] = []
    skills: List[str] = []

Example sample (description was replaced with ... for better readability):

query	location	job_id	job_index	link	apply_link	title	company	company_link	company_img_link	place	description	description_html	date	insights	skills
Python	South Korea	3959499221	0	https://www.linkedin.com/jobs/view/3959499221/?trk=flagship3_search_srp_jobs		Senior Python Software Engineer	Canonical		https://media.licdn.com/dms/image/v2/C560BAQEbIYAkAURcYw/company-logo_100_100/company-logo_100_100/0/1650566107463/canonical_logo?e=1734566400&v=beta&t=emb8cxAFwBnOGwJ8nTftd8ODTFDkC_5SQNz-Jcd8zRU	Seoul, Seoul, South Korea (Remote)	...	...		[Remote Full-time Mid-Senior level, Skills: Python (Programming Language), Computer Science, 8 more, See how you compare to 18 applicants. Try Premium for RSD0, , Am I a good fit for this job?, How can I best position myself for this job?, Tell me more about Canonical]	[Back-End Web Development, Computer Science, Engineering Documentation, Kubernetes, Linux, MLOps, OpenStack, Python (Programming Language), Technical Documentation, Web Services]

Was generated with the following nu shell command:

# Replaces description of a job with elipsis
def hide-description [] {
    update description { |row| '...' } 
    | update description_html { |row| '...' } 
}

cat result.json 
| from  json 
| first 
| hide-description
| to md --pretty 

Last steps before analysis

We already have several ready to use features (title and skills), but I want more:

• Years of experience
• Degree
• Tech stack
• Position
• Responsibilities

So let's add them with help of ChatGPT!

import json
import logging
import os

from dotenv import load_dotenv
from linkedin_jobs_scraper.events import EventData
from openai import OpenAI
from tqdm import tqdm

load_dotenv()

client = OpenAI(
    api_key=os.environ["OPENAI_API_KEY"],
)

with open("result.json", "rb") as f:
    jobs = json.load(f)

parsed_descriptions = []

for job in tqdm(jobs):
    job = EventData(**job)
    chat_completion = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {
                "role": "user",
                "content": """
                    Process given IT job description. 
                    Output only raw JSON with the following fields:
                        - Experience (amount of years or null)
                        - Degree requirement (str if found else null)
                        - Tech stack (array of strings)
                        - Position (middle, senior, lead, manager, other (describe it))
                        - Core responsibilites (array of strings)

                    Output will be passed directrly to the
                    Python's `json.loads` function. So DO NOT APPLY MARKDOWN FORMATTING
                    Example:
                    ```


                    {
                        "experience": 5, 
                        "degree": "bachelor", 
                        "stack": ["Python", "FastAPI", "Docker"], 
                        "position": "middle",
                        "responsibilities": ["Deliver features", "break production"]
                    }


                    ```

                    Here is a job description:
                """
                + "\n\n"
                + job.description_html,
            }
        ],
    )

    content = chat_completion.choices[0].message.content
    try:
        if not content:
            print("Empty result from ChatGPT")
            continue
        result = json.loads(content)
    except json.decoder.JSONDecodeError as e:
        logging.error(e, chat_completion)
        continue

    result["job_id"] = job.job_id
    parsed_descriptions.append(result)

with open("job_descriptions_analysis.json", "w") as f:
    json.dump(parsed_descriptions, f)

Do not forget to add OPENAI_API_KEY to the .env file

Now we can merge by job_id results with data from LinkedIn:

cat job_descriptions_analysis.json 
| from json 
| merge (cat result.json | from json)
| to json
| save full.json

Our data is ready to analyze!

cat full.json | from json | columns
╭────┬──────────────────╮
│  0 │ experience       │
│  1 │ degree           │
│  2 │ stack            │
│  3 │ position         │
│  4 │ responsibilities │
│  5 │ job_id           │
│  6 │ query            │
│  7 │ location         │
│  8 │ job_index        │
│  9 │ link             │
│ 10 │ apply_link       │
│ 11 │ title            │
│ 12 │ company          │
│ 13 │ company_link     │
│ 14 │ company_img_link │
│ 15 │ place            │
│ 16 │ description      │
│ 17 │ description_html │
│ 18 │ date             │
│ 19 │ insights         │
│ 20 │ skills           │
╰────┴──────────────────╯

Analysis

For the start

let df = cat full.json | from json

Now we can see technologies frequency:

$df
| get 'stack' 
| flatten 
| uniq --count 
| sort-by count --reverse 
| first 20 
| to md --pretty

value	count
Python	185
Java	70
AWS	65
Kubernetes	61
SQL	54
C++	46
Docker	42
Linux	41
React	37
Kotlin	34
JavaScript	30
C	30
Kafka	28
TypeScript	26
GCP	25
Azure	24
Tableau	22
Hadoop	21
Spark	21
R	20

With Python:

$df
| filter-by-intersection 'stack' ['python']
| get 'stack' 
| flatten 
| where $it != 'Python' # Exclude python itself
| uniq --count 
| sort-by count --reverse 
| first 10
| to md --pretty

value	count
Java	44
AWS	43
SQL	40
Kubernetes	36
Docker	27
C++	26
Linux	24
R	20
GCP	20
C	18

Without Python:

$df
| filter-by-intersection 'stack' ['python'] --invert
| get 'stack' 
| flatten 
| uniq --count 
| sort-by count --reverse 
| first 10
| to md --pretty

value	count
React	31
Java	26
Kubernetes	25
TypeScript	23
AWS	22
Kotlin	21
C++	20
Linux	17
Docker	15
Next.js	15

The most of the jobs require Python, but there are some front-end, Java and C++ jobs

Magic filter-by-intersection function is a custom one and allow filtering list values that include given set of elements:

# Filters rows by intersecting given `column` with `requirements`
# Case insensitive and works only if ALL requirements exist in a `column` value
# If `--invert` then works as symmetric difference
def filter-by-intersection [
    column: string
    requirements: list<string>
   --invert (-i)
] {
    let required_stack = $requirements | par-each { |el| str downcase }
    let required_len = if $invert { 0 } else { ($requirements | length )}
    $in
    | filter { |row| 
        $required_len == (
            $row 
            | get $column 
            | par-each { |el| str downcase } 
            | where ($it in $requirements) 
            | length
        )
    }
}
</string>

What about experience and degree requirement for each position in Python?

$df
| filter-by-intersection 'stack' ['python'] 
| group-by 'position' --to-table
| insert 'group_size' { |group| $group.items | length } 
| where 'group_size' >= 10
| insert 'experience' { |group| 
    $group.items 
    | get 'experience'
    | uniq --count  
    | sort-by 'count' --reverse 
    | update 'value' { |row| if $row.value == null { 0 } else { $row.value }}
    | rename --column { 'value': 'years' }
    | first 3 
} 
| insert 'degree_requirement' { |group| 
    $group.items 
    | each { |row| $row.degree != null } 
    | uniq --count 
    | sort-by 'value'
    | rename --column { 'value': 'required' }
}
| sort-by 'group_size' --reverse 
| select 'group' 'group_size' 'experience' 'degree_requirement'

Output:

╭───┬────────┬────────────┬───────────────────────┬──────────────────────────╮
│ # │ group  │ group_size │      experience       │    degree_requirement    │
├───┼────────┼────────────┼───────────────────────┼──────────────────────────┤
│ 0 │ senior │         83 │ ╭───┬───────┬───────╮ │ ╭───┬──────────┬───────╮ │
│   │        │            │ │ # │ years │ count │ │ │ # │ required │ count │ │
│   │        │            │ ├───┼───────┼───────┤ │ ├───┼──────────┼───────┤ │
│   │        │            │ │ 0 │     5 │    30 │ │ │ 0 │ false    │    26 │ │
│   │        │            │ │ 1 │     0 │    11 │ │ │ 1 │ true     │    57 │ │
│   │        │            │ │ 2 │     7 │    11 │ │ ╰───┴──────────┴───────╯ │
│   │        │            │ ╰───┴───────┴───────╯ │                          │
│ 1 │ other  │         14 │ ╭───┬───────┬───────╮ │ ╭───┬──────────┬───────╮ │
│   │        │            │ │ # │ years │ count │ │ │ # │ required │ count │ │
│   │        │            │ ├───┼───────┼───────┤ │ ├───┼──────────┼───────┤ │
│   │        │            │ │ 0 │     0 │     8 │ │ │ 0 │ false    │    12 │ │
│   │        │            │ │ 1 │     5 │     1 │ │ │ 1 │ true     │     2 │ │
│   │        │            │ │ 2 │     3 │     1 │ │ ╰───┴──────────┴───────╯ │
│   │        │            │ ╰───┴───────┴───────╯ │                          │
│ 2 │ lead   │         12 │ ╭───┬───────┬───────╮ │ ╭───┬──────────┬───────╮ │
│   │        │            │ │ # │ years │ count │ │ │ # │ required │ count │ │
│   │        │            │ ├───┼───────┼───────┤ │ ├───┼──────────┼───────┤ │
│   │        │            │ │ 0 │     0 │     5 │ │ │ 0 │ false    │     6 │ │
│   │        │            │ │ 1 │    10 │     4 │ │ │ 1 │ true     │     6 │ │
│   │        │            │ │ 2 │     5 │     1 │ │ ╰───┴──────────┴───────╯ │
│   │        │            │ ╰───┴───────┴───────╯ │                          │
│ 3 │ middle │         10 │ ╭───┬───────┬───────╮ │ ╭───┬──────────┬───────╮ │
│   │        │            │ │ # │ years │ count │ │ │ # │ required │ count │ │
│   │        │            │ ├───┼───────┼───────┤ │ ├───┼──────────┼───────┤ │
│   │        │            │ │ 0 │     3 │     4 │ │ │ 0 │ false    │     4 │ │
│   │        │            │ │ 1 │     5 │     3 │ │ │ 1 │ true     │     6 │ │
│   │        │            │ │ 2 │     2 │     2 │ │ ╰───┴──────────┴───────╯ │
│   │        │            │ ╰───┴───────┴───────╯ │                          │
╰───┴────────┴────────────┴───────────────────────┴──────────────────────────╯

Extraction of the most common requirements wasn't as easy as previous steps. So I've met a classification problem, and I'm going to describe my solution in the next chapter of this article.

Conclusion

We successfully extracted and analyzed job data from LinkedIn using the linkedin_jobs_scraper package. Responsibilities in the actual dataset are too sparse and need better processing to make functional classes that will help in CV creation. But the given steps already help me a lot with monitoring and applying to the jobs in half-auto mode.

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