Person

The Data Dictionary has the most up-to-date field definitions for this table. Use it as your primary reference for column names and descriptions.

Column names in this table have no _PER suffix. Use field names exactly as listed below — do not append _PER to any field when querying RELEASE.PER_LATEST directly. The _PER suffix appears only in the pre-joined VELOCITY_* tables, where it disambiguates person fields after a merge — never in PER_LATEST itself.

The person table contains individual person records with professional contact information and email verification data. This is the primary table for building prospect lists, outbound campaigns, and enrichment workflows. Each row is one person at one organization; if a contact holds multiple current positions, they appear in multiple rows.

Where the data comes from

Person records are built from professional profiles, public sources, and verified email and phone data. RevenueBase continuously verifies email deliverability using its internal email verification system and re-verifies addresses on a regular cadence. Titles, seniority, and department are normalized from raw job titles so you can filter and segment consistently.

Person counts by region

The map below shows current person counts by country. Use it to understand coverage and plan targeting by region.

Hide View interactive map: person counts by country

Table stats

Metric	Value
Total records	See map above; updated with each release
Update frequency	Monthly (new delivery on the 1st of each month)
Primary key	`RBID_PAO`
Main foreign key	`RBID_ORG` → `ORG_LATEST.RBID`

Data dictionary

For the complete and most current field reference, see the Data Dictionary. Fill rate is the percentage of rows where a field is non-null. Rates vary by segment (e.g. US and larger organizations often have higher fill rates for phone and seniority).

Identifiers

Field	Description	Example
`RBID`	RevenueBase’s unique identification for the person. The RBID stays the same if someone changes their job.	rb-pabq6o2xf
`RBID_ORG`	RevenueBase’s unique identifier for an organization	rb-oab75n3pe
`RBID_PAO`	RevenueBase’s unique identifier for the person at a specific organization.	rb-pabq6o2xf-rb-oab75n3pe
`LINKEDIN_URL`	The person’s linkedin URL	https://www.linkedin.com/in/simon%c3%a9-burger-ca-sa-0a3a13158
`LINKEDIN_NUM_ID`	LinkedIn’s permanent numeric Member ID (also known as Member Number, LinkedIn ID, or Person ID). Unlike vanity profile URLs, this ID never changes — making it the most reliable key for deduplication and cross-system matching. ~95% fill rate, null values are possible for small amount of profiles	599793536

Name & demographics

Field	Description	Example
`FIRST_NAME`	The person’s first name	Marc
`MIDDLE_NAME`	The person’s middle name	F.
`LAST_NAME`	The person’s last name	Rojas
`FULL_NAME`	The person’s full name	Kathryn E. Gibson
`TITLE_NAME`	The person’s name prefix	Dr.
`SUFFIX_NAME`	The person’s name suffix	Ph. D
`LINKEDIN_HEADLINE`	The person’s Linkedin headline	Sales Executive, Rapid Diagnostics, Abbott
`LINKEDIN_CONNECTIONS_COUNT`	Number of LinkedIn connections the person has	500
`NICKNAME_NAME`	The person’s nickname	Mike
`ABOUT_ME`	Linkedin personal profile about me field	Experienced Transportation Security Officer with a demonstrated history of working in the government administration industry. Skilled in Negotiation, Team Building, Public Speaking, Management, and Marketing. Strong military and protective services professional with a Doctor of Philosophy - PhD focused in Public Service Leadership Emergency Management from Capella University.

Professional info

Field	Description	Example
`JOB_TITLE`	The person’s current job title	Senior Technical Consultant
`JOB_LEVEL`	The seniority level of the person’s job	Manager
`JOB_FUNCTION`	The department that the person works in.	Information Technology
`PERSONA`	Classification of person job type by several business areas	Sales Engineer/Solution Architect, Information Technology
`JOB_DESCRIPTION`	The description of the person’s current job	Financial analysts,tax analysts,account payable
`JOB_IS_CURRENT`	Indicates whether the job is currently active	TRUE
`JOB_START_DATE`	When the person began their job	2013-01-01 00:00:00
`JOB_COUNT`	The number of jobs a person has had	4
`JOB_END_DATE`	When the person ended their most recent job	2022-06-01
`JOB_ORDER_IN_PROFILE`	Position of this job in the person’s LinkedIn profile	1
`LINKEDIN_INDUSTRY`	The Linkedin industry associated with the person’s employer	Information Technology & Services

Contact information

Field	Description	Example
`EMAIL_ADDRESS`	The person’s work email address	some_valid_email@somedomain.com
`EMAIL_DOMAIN`	The domain of the person’s work email address	somedomain.com
`EMAIL_STATUS`	The verification status of the email. If an email exists in the dataset, it has been verified as valid.	VALID
`EMAIL_LAST_VERIFIED_AT`	The contact’s email last verified timestamp. Timestamp specifically designed to indicate last revalidation of email at RevenueBase side	2013-01-01 00:00:00
`DIRECT_PHONE`	The direct office contact number	+1 612-876-4679
`CELLPHONE`	Mobile phone number	+1 352-359-7618

Addresses (contact location)

The first set of address fields (CITY, STATE_NAME, STATE_CODE, COUNTRY_NAME, COUNTRY_CODE, COUNTRY_REGION, CONTINENT) describes where the person is located on their professional profile. The fields prefixed with JOB_LOCATION_ describe where the person works from — usually the office location if they work from an office. These two address sets do not always match: someone might live in Boston, MA and work in Providence, RI. The person’s location (the first set) typically has a higher fill rate, so use those fields when you search for people by location.

Field	Description	Example
`CITY`	The city where the person lives	Lexington
`STATE_NAME`	The state/province/region of the country that the person is located in	District of Columbia
`STATE_CODE`	The abbreviation of the state/province/region of the country that the person is located in	DC
`COUNTRY_NAME`	The name of the country the person is located in	United States
`COUNTRY_CODE`	The code for the country the person is located in	US
`COUNTRY_REGION`	The global region that the person is located in	NORAM
`CONTINENT`	The continent that the person is located in	North America
`JOB_LOCATION_CITY`	The city in where the person works	New York
`JOB_LOCATION_STATE`	Name of State or Province of the country that the person is located	New York
`JOB_LOCATION_STATE_CODE`	The state/province/region of the country that the person is located in	NY
`JOB_LOCATION_COUNTRY`	The country name where the person works	South Africa
`JOB_LOCATION_COUNTRY_CODE`	The country code where the person works	ZA
`JOB_LOCATION_COUNTRY_REGION`	The geographic region that a contact works in	EMEA
`JOB_LOCATION_CONTINENT`	The continent where the person works	North America

Organization context

Field	Description	Example
`JOB_ORG_LINKEDIN_URL`	The organization’s LinkedIn URL	https://www.linkedin.com/company/company_url
`COMPANY_NAME`	The organization’s name	Meta. Inc

Profile enrichments

Field	Description	Example
`EDUCATION`	The person’s education	[ { ""degree"": ""Bachelor’s degree, Biology"", ""end_date"": ""2003-01-01"", ""organization"": ""Louis Pasteur University Strasbourg"", ""start_date"": ""1999-01-01"" }, { ""degree"": ""Ph.D, Biochemistry"", ""description"": ""Advisor: Daniel Kern Thesis title: New insights in tRNA-dependent asparagine biosynthesis."", ""end_date"": ""2008-01-01"", ""organization"": ""Molecular and Cellular Biology Institute (IBMC)"", ""start_date"": ""2004-01-01"" }, { ""degree"": ""Master’s degree, Biochemistry"", ""description"": ""I discovered a new type of ribonucleoprotein involved in amino acid biosynthesis in bacteria and archea."", ""end_date"": ""2004-01-01"", ""organization"": ""Louis Pasteur University Strasbourg"", ""start_date"": ""2003-01-01"" }]
`CERTIFICATIONS`	Linkedin personal profile certifications	[ { ""authority"": ""Capella University"", ""linkedin_url"": ""https://www.linkedin.com/company/capella-university"", ""name"": ""Doctoral Candidate"", ""start_date"": ""2021-03-01"" }]
`LANGUAGES`	The languages spoken	[ { ""language"": ""english"", ""proficiency"": ""native or bilingual proficiency"" }, { ""language"": ""german"", ""proficiency"": ""limited working proficiency"" }]
`SKILLS`	The person’s job skills	[ ""media tours"", ""microsoft office"", ""research"", ""photoshop"", ""photography"", ""media production"", ""social media"", ""microsoft excel"", ""safety training"", ""microsoft word"", ""hostile environment safety"", ""journalism"", ""powerpoint"", ""first aid"", ""public speaking"", ""leadership"" ]
`INTERESTS`	Linkedin personal profile interests field	[ ""Playing Music"" ]
`PATENTS`	Any patents listed on someone’s Linkedin profile	[ { ""title"": ""Tagged Credit Profile System For Credit Applicants"", ""url"": ""https://patents.google.com/patent/US20090327120A1/&urlhash=H5PF&trk=public_profile_patent-title"" }, { ""date"": ""2004-07-01"", ""title"": ""On-line Distribution Of Per-use Media With Flexible Purchase Options"", ""url"": ""http://www.google.com/patents/US20050027616&urlhash=HU_b&trk=public_profile_patent-title"" }]
`PUBLICATIONS`	Any publications listed on someone’s Linkedin profile	[ { ""date"": ""2018-07-01"", ""title"": ""Impact Des Assistantes Humanitaire Sur L’attentisme De Déplace Interne Dela Rdc"" }]
`WEBSITES`	The person’s personal website	`[{"url": "https://www.example.com"}, {"url": "http://personal.blogspot.com/"}]`
`LOGO_URL`	The person’s linkedin profile picture URL	https://us.cdn.com/ca-sa-0a3a13158

Metadata

Field	Description	Example
`UPDATED_AT`	The person’s last update timestamp. Any field update (except email and phone related fields) will trigger a change in this value. LinkedIn successful profile revalidation (even with no updates) will also update this timestamp	2013-01-01 00:00:00
`REVENUEBASE_CONTACT_VERIFICATION_SUMMARY`	Verification Summary showing details on a record observed specifing when and how it was verified	Verified by RevenueBase \| Experience last observed on LinkedIn on September 7, 2025: Proposal Manager at Metaphase \| Profile: linkedin.com/in/jamie-gregory-28878356 \| Email last verified on October 15, 2025 via RevenueBase’s Email Verification Tool \| Next verification scheduled: November, 2025 \| Learn more at https://revenuebase.ai/verified-by-revenuebase

Understanding verification fields

All emails in the dataset have been verified as valid by RevenueBase’s email verification system. If an email address exists in the dataset, it means it has passed verification and is considered deliverable.

For the safest outbound sends, filter on EMAIL_LAST_VERIFIED_AT >= DATEADD(day, -60, CURRENT_DATE()) to prioritize recently verified addresses. Since all emails in the dataset are valid, you don’t need to filter on EMAIL_STATUS.

Joining this table

Join PER_LATEST to ORG_LATEST on RBID_ORG = RBID when you need full firmographic or HQ address data. Join to insights_latest on RBID_ORG = RBID_ORG when you need organization-level insight or intent signals.

Person + Organization (full organization attributes)

SELECT
    c.RBID,
    c.FIRST_NAME,
    c.LAST_NAME,
    c.EMAIL_ADDRESS,
    c.JOB_TITLE,
    co.COMPANY_NAME,
    co.INDUSTRY_LINKEDIN,
    co.EMPLOYEE_COUNT_MAX,
    co.HEADQUARTERS_CITY,
    co.HEADQUARTERS_COUNTRY_NAME
FROM RELEASE.PER_LATEST c
JOIN RELEASE.ORG_LATEST co ON c.RBID_ORG = co.RBID
WHERE co.EMPLOYEE_COUNT_MAX BETWEEN 100 AND 1000;

How to calculate fill rates

Run a query that counts non-null values and divides by total rows. Use this pattern to inspect any field or segment.

SELECT
    COUNT(*) AS total_records,
    ROUND(COUNT(EMAIL_ADDRESS) * 100.0 / COUNT(*), 1) AS email_fill_pct,
    ROUND(COUNT(CELLPHONE) * 100.0 / COUNT(*), 1) AS phone_fill_pct,
    ROUND(COUNT(JOB_TITLE) * 100.0 / COUNT(*), 1) AS title_fill_pct,
    ROUND(COUNT(JOB_FUNCTION) * 100.0 / COUNT(*), 1) AS department_fill_pct
FROM RELEASE.PER_LATEST;

Sample queries

Build a prospect list (VP+ in engineering or sales at mid-size software organizations)

What you’re finding: Decision-makers and influencers at software organizations in a size band that fits your ICP. Why these fields: All emails in the dataset are verified as valid. EMAIL_LAST_VERIFIED_AT helps prioritize recently verified addresses. JOB_LEVEL and JOB_FUNCTION narrow to buying roles. LINKEDIN_INDUSTRY_ORG and EMPLOYEE_COUNT_MAX (denormalized) avoid a join while filtering by ICP. Logic: Restrict to verified emails, seniority in C-Suite/VP/Director, departments that typically own tooling decisions, and a single industry + employee range. Order by EMAIL_LAST_VERIFIED_AT so the freshest data appears first.

SELECT
    PER.FIRST_NAME,
    PER.LAST_NAME,
    PER.EMAIL_ADDRESS,
    PER.JOB_TITLE,
    PER.JOB_LEVEL,
    PER.COMPANY_NAME,
    ORG.EMPLOYEE_COUNT_MAX
FROM RELEASE.PER_LATEST AS PER
INNER JOIN RELEASE.ORG_LATEST AS ORG
ON PER.RBID_ORG = ORG.RBID
WHERE JOB_LEVEL IN ('C-Team', 'VP', 'Director')
  AND JOB_FUNCTION IN ('Engineering', 'Sales')
  AND LINKEDIN_INDUSTRY LIKE '%Software%'
  AND EMPLOYEE_COUNT_MAX BETWEEN 100 AND 1000
ORDER BY EMAIL_LAST_VERIFIED_AT DESC
LIMIT 500;

Check fill rates for a segment

What you’re finding: How complete key fields are for a subset (e.g. one industry or country) so you can set expectations for list build size and which filters to use. Why these fields: Same as the global fill-rate query, but run with a WHERE clause so rates reflect only the segment you care about.

SELECT
    COUNT(*) AS total_records,
    ROUND(COUNT(EMAIL_ADDRESS) * 100.0 / COUNT(*), 1) AS email_fill_pct,
    ROUND(COUNT(CELLPHONE) * 100.0 / COUNT(*), 1) AS phone_fill_pct,
    ROUND(COUNT(JOB_TITLE) * 100.0 / COUNT(*), 1) AS title_fill_pct
FROM RELEASE.PER_LATEST
WHERE LINKEDIN_INDUSTRY LIKE '%Software%'
  AND COUNTRY_NAME = 'United States';

Getting Started

Base Tables

Pre-Joined Tables

Joining Tables

Where the data comes from

Person counts by region

Table stats

Data dictionary

Identifiers

Name & demographics

Professional info

Contact information

Addresses (contact location)

Organization context

Profile enrichments

Metadata

Understanding verification fields

Joining this table

Person + Organization (full organization attributes)

How to calculate fill rates

Sample queries

Build a prospect list (VP+ in engineering or sales at mid-size software organizations)

Check fill rates for a segment

​Where the data comes from

​Person counts by region

​Table stats

​Data dictionary

​Identifiers

​Name & demographics

​Professional info

​Contact information

​Addresses (contact location)

​Organization context

​Profile enrichments

​Metadata

​Understanding verification fields

​Joining this table

​Person + Organization (full organization attributes)

​How to calculate fill rates

​Sample queries

​Build a prospect list (VP+ in engineering or sales at mid-size software organizations)

​Check fill rates for a segment

Where the data comes from

Person counts by region

Table stats

Data dictionary

Identifiers

Name & demographics

Professional info

Contact information

Addresses (contact location)

Organization context

Profile enrichments

Metadata

Understanding verification fields

Joining this table

Person + Organization (full organization attributes)

How to calculate fill rates

Sample queries

Build a prospect list (VP+ in engineering or sales at mid-size software organizations)

Check fill rates for a segment