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Available fields

Advanced Dataset Creator exposes ~1,500 fields across 9 datasets. Any field on this page can appear in filters, output columns, aggregations, and computed expressions when you use --agent=advanced_dataset_creator.Notation
  • Dot notation denotes nested objects — e.g. acquired_by_summary.acquirer_name.
  • .element marks items inside an array — e.g. funding_rounds.element.name refers to the name field on each entry in the funding_rounds array.
  • {placeholder} in field patterns denotes a set of known values listed above the table — substitute one to get the actual field name.

Company

777 fields covering firmographics, workforce analytics, financials, web traffic, hiring signals, technographics, employee reviews, funding history, executive movement, salary benchmarks, and social presence.

Core identity

Location

Contact info and web presence

Employee count

Employee count by department

Current breakdown and monthly time series across 20 departments.Departments available: medical, sales, hr, legal, marketing, finance, technical, consulting, operations, product, general_management, administrative, customer_service, project_management, design, research, trades, real_estate, education, other_department

Employee count by seniority

Current breakdown and monthly time series across 12 seniority levels.Levels available: owner, founder, clevel, partner, vp, head, director, manager, senior, intern, specialist, other_management

Employee count by region

Current breakdown and monthly time series across 18 global regions.Regions available: eastern_europe, latin_america, southern_europe, sub_saharan_africa, central_asia, northern_america, australia_new_zealand, northern_europe, south_eastern_asia, polynesia, southern_asia, northern_africa, melanesia, western_europe, western_asia, eastern_asia, micronesia, unknown

Employee count by country

Employee headcount deltas

Headcount change analytics across 5 dimensions, each sharing the same field structure.Dimensions ({dim}): all, country, department, region, seniority Periods ({period}): 1m, 3m, 6m, 9m, 12m

Hiring and job postings

Key executives and movement

Talent flow

Revenue and financials

Income statements

Funding

Funding rounds (all)

IPO and stock

Acquisition

Web traffic

Visitor demographics

Technologies

Competitors

Product and pricing

Product reviews

Employee reviews

Aggregate scores, breakdowns across 9 dimensions, and monthly time series with change metrics for each dimension.Breakdown dimensions: business_outlook, career_opportunities, ceo_approval, compensation_benefits, culture_values, diversity_inclusion, recommend, senior_management, work_life_balance

Salary data

Social followers

Company updates (LinkedIn posts)

Topics


Person

271 fields covering professional identity, current and historical employment, education, skills, certifications, publications, patents, GitHub activity, salary projections, and social activity.

Core identity

Location

Social and web

Current employment

Full experience history

Career changes

Recent experience changes

Experience breakdown

Education

Skills and interests

Certifications

Languages

Patents

Publications

Projects

Awards

Courses

Organizations

Recommendations

Activity

GitHub repos

Salary projections


Job posting

66 fields covering active and historical job listings with titles, locations, functions, industries, seniority, salary, and status tracking.

Job functions

Job industries

Job status log


Conference member

37 fields covering conference attendees with company and contact matching.

Walmart seller

27 fields covering Walmart Marketplace seller profiles.

CRE broker

21 fields covering commercial real estate brokerages.

CRE property manager

16 fields covering commercial real estate property management firms.

CRE developer

15 fields covering commercial real estate developers.

CRE apartment owner

9 fields covering apartment portfolio owners.

Referencing fields in queries

Fields can be referenced by exact name in a natural-language prompt to --agent=advanced_dataset_creator. The agent translates the prompt — including any explicit field names, operators, and values — into SQL.
Exact field names give precise control over filters and output columns. Natural-language descriptions (e.g. “technical headcount” instead of employees_count_breakdown_by_department.employees_count_technical) also work — the agent resolves them to the corresponding fields.