DEV Community: Zebin Wang

How to Export Remote Job Salary Data to CSV (Himalayas + Remote OK)

Zebin Wang — Thu, 08 Oct 2026 09:30:19 +0000

If you're job hunting remotely, or building anything around remote work, salary data is the part you actually want and the part that's hardest to collect. Two job boards publish it in a usable form: Himalayas (salary range, currency, period, and seniority) and Remote OK (salary range in USD, plus tags).

I built two small Apify Actors that turn those listings into clean rows. Here's how to get the salary data into a CSV and do something useful with it.

What each source gives you

Himalayas Remote Jobs Scraper
title, companyName, employmentType, seniority, minSalary, maxSalary, currency, salaryPeriod, isWorldwide, publishedAt, url

Remote OK Jobs Scraper
title, company, location, tags, salaryMinUsd, salaryMaxUsd, publishedAt, url

Himalayas is the better source when seniority matters and when you need to know whether a role is open worldwide (isWorldwide). Remote OK is handy when you want everything already in USD and filterable by tech tags.

Step 1: Run the scrapers

In the browser, open either Actor, click Try for free, fill in the input, and hit Start. Then export the results as CSV.

Or from Python:

pip install apify-client pandas

from apify_client import ApifyClient
import pandas as pd

client = ApifyClient("YOUR_APIFY_TOKEN")

def run(actor_id, run_input):
    r = client.actor(actor_id).call(run_input=run_input)
    return list(client.dataset(r["defaultDatasetId"]).iterate_items())

himalayas = pd.DataFrame(run("slate_cyclone/himalayas-jobs-scraper",
    {"keywords": "python", "salaryRequired": True, "maxJobs": 100}))
remoteok = pd.DataFrame(run("slate_cyclone/remoteok-jobs-scraper",
    {"tags": ["python", "react"], "salaryRequired": True, "maxJobs": 100}))

Step 2: Keep only rows with salary data

h = himalayas.dropna(subset=["minSalary", "maxSalary"])
r = remoteok.dropna(subset=["salaryMinUsd", "salaryMaxUsd"])

Step 3: Answer real questions

Median salary range by seniority on Himalayas (yearly, USD only):

yearly_usd = h[(h["currency"] == "USD") & (h["salaryPeriod"].str.lower() == "year")]
print(yearly_usd.groupby("seniority")[["minSalary", "maxSalary"]].median())

Only roles open worldwide:

print(h[h["isWorldwide"] == True][["title", "companyName", "minSalary", "maxSalary", "url"]])

Remote OK salaries for a given tag, for example python:

py = r[r["tags"].apply(lambda t: "python" in [x.lower() for x in (t or [])])]
print(py[["title", "company", "salaryMinUsd", "salaryMaxUsd"]].describe())

Step 4: Save it

h.to_csv("himalayas_salaries.csv", index=False)
r.to_csv("remoteok_salaries.csv", index=False)

Cost

Both Actors are pay-per-result: $0.80 per 1,000 jobs.

Check the actual values in your own export before you rely on a filter. Salary periods and currencies vary by listing, so filter them explicitly, as above, rather than assuming everything is yearly USD.

This post was written with AI assistance.

How to Scrape Job Listings from Workday Career Sites (Without Writing a Scraper)

Zebin Wang — Thu, 08 Oct 2026 09:27:38 +0000

A lot of large companies run their careers page on Workday. If you've ever tried to track openings across several of them, you know the pain: every site looks the same but lives on its own subdomain, the pages render with JavaScript, and copying listings by hand doesn't scale.

I needed this for my own job search, so I built a small Apify Actor that pulls Workday job listings into structured data. This post shows how to use it from the browser and from Python.

What you get

Each job comes back with these fields:

Field	What it is
`title`	Job title
`company`	Employer
`location`	Location as listed
`country`	Country
`timeType`	Full time / part time
`postedOn`	Posting label as shown on the site
`postedDate`	Posting date
`jobId`	Workday job ID
`url`	Direct link to the posting

Option 1: Run it in the browser

Open Workday Jobs Scraper and click Try for free.
Fill in the input form.
Click Start. When the run finishes, export the results as CSV, JSON, or Excel.

Option 2: Run it from Python

pip install apify-client

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run_input = {
    "careerSiteUrls": ["https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite"],
    "searchText": "data",
    "maxJobsPerSite": 100
}

run = client.actor("slate_cyclone/workday-jobs-scraper").call(run_input=run_input)

for job in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(job["postedDate"], job["company"], job["title"], job["url"])

Ideas for using the data

Watch target companies. Schedule the Actor daily on Apify and compare jobIds to spot new openings.
Build a niche job board. Filter by country or timeType and publish what's left.
Look at hiring trends. Count postings per company or location over time.

Cost

It's pay-per-result: $0.80 per 1,000 jobs, and you only pay for what you scrape.

If you run into a Workday site it doesn't handle, leave a comment and I'll take a look.

This post was written with AI assistance.

The Job Application Tracker I Wish I'd Started With (Free Notion Template)

Zebin Wang — Thu, 08 Oct 2026 09:18:17 +0000

The most tiring part of a job search usually isn't writing the resume. It's that everything is scattered. Recruiter messages live in one app, interview invites in your inbox, and the list of companies in a notes file. Every morning starts with the same question: who hasn't replied yet, and who am I talking to next week?

After a few weeks of that, I cut my tracking down to the smallest setup that still worked. Here it is.

The minimum setup

1. One status per application.
Wishlist → Applied → Assessment → Interviewing → Offer, or Rejected. Every application sits in exactly one status. If you can't say which status a company is in, you haven't really followed up.

2. Always fill in the next action and its date.
"Waiting to hear back" isn't progress. Progress is something you do: follow up with X today, send a portfolio to Y by Friday. A follow-up without a date usually never happens.

3. One hard rule.
After two follow-ups with no answer, mark it Rejected and move on. No more "I'll give it another week." It keeps you from running on false hope.

4. A weekly check of three numbers.
New applications this week, companies at the interview stage, and applications that haven't moved in 7 days or more. Every stale one gets a decision: follow up once more, or close it.

That's it. The goal isn't to fill in every field. It's to keep "still waiting on them" separate from "I need to act."

Free template

I turned the minimum setup into a Notion tracker you can duplicate for free. It has the six statuses, Next Action and Next Action Date fields, an automatic days-since-applied count, a follow-up reminder, and both a pipeline board and a calendar view:

https://different-epoxy-5a6.notion.site/Job-Application-Tracker-Free-Lite-3f3ba46009bd8114882dc9dbfaa91212

If you want the full version

Disclosure: I built this, and the full version is paid. On top of the tracker it adds:

an interview log, so you can open one page before each round and see what was asked last time
a weekly review page
a company database that counts applications per company
resume versions, so you know which resume went where
contacts
English and Chinese versions

It's $9 here: Job Search Tracker — Notion Template (EN/ZH)

Honestly, the four rules above matter more than any template. Start with the free tracker, and if it sticks, the full version saves you building the rest yourself.

What's your current setup: a spreadsheet, notes, or nothing yet? Tell me in the comments.

This post was written with AI assistance.