Email lists by job title: 57 US lists, counted and free

The people of each title with an email or a mobile phone, at companies of 11 to 500 employees in the free file; then the 50M+ dataset, every size. A person can be in two lists (“Owner & CEO”).

Sign up with your work email and unzip the 10M+ U.S. dataset into a folder named us_10m. Then, in Python with pip install duckdb, a pattern on the title writes its people to a CSV. Chiropractors, for one:

import duckdb

# Any title: a pattern on the title as written on LinkedIn, in any letter case. \b marks a word's edge
duckdb.sql(r"""
  copy (
    select p.FULL_NAME, p.CURRENT_JOB_TITLE, c.NAME as COMPANY, c.URL as WEBSITE, p.EMAIL, p.MOBILE_PHONE, p.STATE_CODE, p.LINKEDIN_URL
    from 'us_10m/us_smb_mid_market_persons.parquet' p
    join 'us_10m/us_smb_mid_market_companies.parquet' c on c.LINKEDIN_ID = p.CURRENT_JOB_COMPANY_LINKEDIN_ID
    where regexp_matches(p.CURRENT_JOB_TITLE, '\bchiropractor\b', 'i')
      and (p.EMAIL is not null or p.MOBILE_PHONE is not null)
  ) to 'us_chiropractors.csv'
""")

Every column of the file and its fill rate: the free US B2B leads dataset. Companies by industry and state: the free list of US companies. A list cut further, by title, industry and state together: a leads list in one SQL query.