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HIRING INTELLIGENCE / MANAGED DATA

Indeed
job data.

Turn job postings into a clearer view of hiring demand.

We collect Indeed job titles, employers, locations, salary details and descriptions for your research and data pipelines.

Discuss your project Explore data points

FROM LISTINGS TO HIRING SIGNALS

Understand who is hiring. And what they need.

Build a source-linked view of advertised roles. Start with the markets and employers that matter to you, then scope the collection around your analysis.

01

Map hiring demand

Compare advertised roles by company and geography. Use a consistent collection scope to examine how demand changes between observations.

02

Research pay & requirements

Read salary wording alongside job descriptions and employment type. Scope skills extraction or role classification when your workflow needs structured signals.

03

Build a job-data pipeline

Bring posting identifiers, URLs and collection times into your warehouse or job research product, with delivery and permitted use agreed up front.

WHAT WE COLLECT

The role. The context.
The original source.

Choose the Indeed data points your workflow needs. Related fields stay together, with the final schema and availability confirmed for your collection scope.

01

Listing identity

Keep each posting traceable to its platform and original page.

View field names
  • Website
  • Job ID
  • URL
02

Role & employer

Connect the advertised job title with the company named in the listing.

View field names
  • Job Title
  • Company
03

Location

Compare hiring across cities, states and countries without losing the source location.

View field names
  • City
  • State
  • Country
04

Pay & employment

Retain the salary wording and job type when available. Missing pay is not a zero salary.

View field names
  • Salary
  • Job Type
05

Job description

Preserve the role description for requirements, responsibility and skills research.

View field names
  • Job Description
06

Posting & collection time

Keep the listed posting date separate from the collection timestamp. Agree timezone conventions before delivery.

View field names
  • Date Posted
  • CacheTime

Need standardized titles, parsed salary ranges or skills labels? We can scope these as enrichment, while keeping source values separate.

Plan your schema

BEFORE THE DATA BECOMES A DECISION

Count postings. Not assumed hires.

A job advertisement is an observed listing, not proof of a new hire, an unfilled position or company growth. Keep those distinctions in the dataset you build on.

Identity before deduplication
Job IDs are source-specific. Cross-board matching needs agreed rules for company, title and location; similar titles alone do not prove a duplicate.
Separate dates and outcomes
Date Posted and CacheTime describe different events. Repeated observations can support change tracking, but a disappearing listing does not prove the role was filled.
Preserve the original wording
Keep source salary and job-type text. Numeric salary ranges, standardized titles and extracted skills are separate transformations, not guaranteed source fields.

A COLLECTION BUILT AROUND YOUR BRIEF

From target roles
to a maintained feed.

A useful job dataset needs defined coverage, source-specific collection and quality checks. We agree each part around your research.

  1. 01 / DEFINE

    Set your market

    Share target countries, locations, employers or role criteria. Agree required fields, permitted access and refresh frequency.

  2. 02 / VALIDATE

    Review the evidence

    Check a sample for identifiers, missing values, descriptions and salary wording. Confirm matching rules and any enrichment separately.

  3. 03 / DELIVER

    Connect your workflow

    Choose CSV, TSV, JSON or a scoped API integration. Agree delivery, monitoring and maintenance before the recurring feed begins.

Need to analyze a few public pages?

The self-service Scraping API collects accessible public content with optional AI analysis. It does not bypass CAPTCHA or login and does not automatically return this job-data schema.

Explore Scraping API

DETAILS THAT CHANGE THE ANALYSIS

Build on the data.
Not assumptions.

Can you identify new, changed and closed jobs?

We can scope repeated collections and comparisons using source-specific IDs and observation times. New and changed records need a consistent coverage window. Missing records are checked against collection failures; a confirmed disappearance is not a confirmed hire or a time-to-fill measurement.

How do you handle reposts and duplicate vacancies?

We retain source identifiers and URLs. A repost may receive a different ID, and similar listings can represent distinct locations or openings. We agree matching rules and preserve the original records so merged groups can be reviewed.

Can you turn descriptions into skills or seniority fields?

Yes, as separately scoped enrichment. We agree a taxonomy and validation sample, retain the original description and distinguish extracted or model-derived labels from source-provided fields. The base collection does not guarantee a dedicated skills or seniority column.

How are coverage, refresh frequency and cost agreed?

We scope countries, employers or search criteria, required fields, expected volume and delivery frequency together. We review a sample and agree coverage and pricing before a recurring collection begins. An accessible sample does not establish complete board-wide coverage.

What happens if a page fails or the source changes?

We agree validation, freshness checks and failure reporting as part of the managed service. An unavailable page or incomplete run is reported separately from an empty result. Maintenance and retry responsibilities are defined in the project scope.

Can we receive the data in our existing schema?

We can scope CSV, TSV or JSON delivery and project-specific API integration. Identifiers remain strings; timestamp conventions, null values, description formatting and salary transformations are agreed before delivery. This is a managed feed, not an automatic output schema of the self-service Scraping API.

EXPAND YOUR HIRING RESEARCH

Cross-board matching is scoped explicitly. Source records stay traceable.

YOUR MARKET. YOUR DATA REQUIREMENTS.

Build your
Indeed
job-data feed.

Tell us which roles, employers and locations matter. We will review access and scope a sample before agreeing scale and delivery.

  • Markets, employers and role criteria
  • Source fields and optional enrichment
  • Refresh schedule and delivery format
Talk through your project

Up to 5,000 characters.

Projects are accepted individually, subject to feasibility and available capacity. An enquiry does not reserve a solution or confirm delivery. Pricing and terms are agreed directly with our team.

A real person reviews your brief and follows up about scope and delivery.

ScrapingBuddy is an independent data service, not affiliated with or endorsed by Indeed. We provide job-data collection, not recruitment or job placement. Hero artwork is illustrative. Access, coverage and delivery commitments are agreed per project.