Steff
A job post is a company telling the world what it's about to spend money on. When a company hires two data engineers, it likely has a data project this quarter, a budget for it and a team that will soon pick its tools. If you sell something that helps with that project, there's hardly a better moment to reach out.
This post shows how to turn public LinkedIn job posts into a weekly list of companies worth contacting, with a reason to write to each one.
Pick the jobs that signal a need
Start from what you sell and ask which new hire would own the problem it solves. A few examples:
| If you sell | Watch for jobs like |
|---|---|
| Data or analytics tools | Data engineer, analytics engineer |
| Security services | Security engineer, SOC analyst |
| Recruiting services | Recruiter, talent acquisition |
| Marketing services | Marketing manager, growth marketer |
| Support software | Customer support lead |
One to three job titles are enough to start.
Step 1: Pull this week's posts
Open the LinkedIn Jobs Scraper and fill in:
- Job title or keywords:
data engineer - Location: a country, region or city, like
Germany - Posted within: Past week
- Number of jobs:
100
You can narrow it further with Experience level, Workplace (on-site, remote or hybrid) and Employment type.
The scraper reads LinkedIn's public job pages, so you don't need a LinkedIn account. Each row has the job title, company, company link, location, posting date, job link, apply link, full job description, experience level, employment type and industries.
Download the results as CSV or Excel.
Step 2: Rank companies, not jobs
One job post is a hint. Three posts for the same kind of role mean a team is being built.
Make a pivot table with companyName as rows and the count of url as values, then sort by the count. The companies at the top are hiring hardest for the problem you solve. Use the industries column to drop companies outside your market.
Step 3: Read the description for your opener
The full description is the most useful part. Companies write down things they'd never tell a salesperson:
- The tools they use: "experience with Snowflake and dbt"
- The project: "you'll rebuild our reporting from the ground up"
- What's urgent: "our first data hire", "to support our expansion into France"
My favorite signal is a line like "our first data hire". It means the team, the tools and the budget are all being decided right now.
To flag a tool across all rows, add a column like this, where J is the description column:
=ISNUMBER(SEARCH("snowflake", J2))
Then write your first line about their project, not your product. "Saw you're hiring to rebuild your reporting" gets read. "We help companies with data" doesn't.
Step 4: Watch your target accounts
Already have a list of target companies? Add them to Only these company IDs, up to 50. A company's ID is the number LinkedIn uses for it. You'll see it after f_C= in the address bar when you look at the company's jobs. With IDs set, the run only returns jobs from those companies, so you'll notice when one of them starts hiring in your area.
Only these companies takes plain names instead, but it only filters the jobs your search finds. Pair it with a specific keyword and location.
Step 5: Make it a weekly routine
Your run is saved as a task in your account. Schedule it for every Monday with Posted within set to Past week, and each list holds that week's new posts. Download the list, or fetch the results through the API and send them to your CRM.
Not in sales?
The same data works for other jobs:
- Job seekers: set Posted within to Past 24 hours and run it daily. New roles arrive in one table with their full descriptions.
- Recruiters and analysts: count posts by experience level, employment type or location to see where demand is moving.
Good to know
- One run returns up to 100 jobs.
- Salary and job function are often empty, because LinkedIn doesn't show them on public job pages.
- Descriptions can include recruiters' names, emails and phone numbers. Treat them as personal data: keep only what you need, and follow privacy law, such as GDPR, in your outreach.
- Runs use your own Selextract credit.