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Candor Scraper

Candor Scraper collects structured salary, offer, and RSU compensation data from Candor to help professionals and analysts understand real-world compensation trends. It simplifies accessing offer insights across companies, roles, and locations, turning raw listings into actionable datasets.

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Introduction

This project gathers compensation-related information such as offers, salary ranges, and RSU rankings based on company, location, and job title inputs. It solves the problem of fragmented and hard-to-compare compensation data by centralizing results into a clean, usable format. The scraper is designed for job seekers, recruiters, compensation analysts, and researchers who need reliable market signals.

Compensation Intelligence and Market Insights

  • Supports targeted searches by company, location, and job title
  • Enables broad or exact-match queries depending on research needs
  • Returns raw, flexible data suitable for analytics pipelines
  • Designed for scalable runs with pagination and filtering
  • Focused on speed, cost-efficiency, and data completeness

Features

Feature Description
Company Search Retrieve compensation data associated with specific companies.
Location Filtering Narrow results by geographic location for localized insights.
Job Title Matching Analyze offers tied to particular roles or seniority levels.
Offer Retrieval Collect salary offers with detailed compensation attributes.
RSU Rankings Access equity and RSU-related ranking data for comparison.
Pagination Control Configure page limits to balance coverage and performance.

What Data This Scraper Extracts

Field Name Field Description
company_name Name of the company associated with the offer.
job_title Role or position title for the compensation entry.
location Geographic location tied to the offer data.
base_salary Reported base salary figure or range.
total_compensation Combined compensation including salary and equity.
rsu_value RSU or equity component associated with the offer.
currency Currency used for all monetary values.
data_source Reference identifier for the originating dataset entry.

Directory Structure Tree

Candor/
β”œβ”€β”€ src/
β”‚   β”œβ”€β”€ main.py
β”‚   β”œβ”€β”€ pipelines/
β”‚   β”‚   β”œβ”€β”€ offers.py
β”‚   β”‚   β”œβ”€β”€ rsu_rankings.py
β”‚   β”‚   └── search.py
β”‚   β”œβ”€β”€ utils/
β”‚   β”‚   β”œβ”€β”€ validators.py
β”‚   β”‚   └── helpers.py
β”‚   └── config/
β”‚       └── settings.example.json
β”œβ”€β”€ data/
β”‚   β”œβ”€β”€ samples/
β”‚   β”‚   └── offers.sample.json
β”‚   └── outputs/
β”‚       └── results.json
β”œβ”€β”€ requirements.txt
└── README.md

Use Cases

  • Job seekers use it to compare offers across companies, so they can negotiate better compensation.
  • Recruiters use it to benchmark roles, so they can design competitive packages.
  • Compensation analysts use it to study salary and RSU trends, so they can produce market reports.
  • HR teams use it to validate internal pay bands, so they can ensure fairness and consistency.
  • Researchers use it to analyze compensation data, so they can identify industry-wide patterns.

FAQs

Does this tool support exact keyword matching for searches? Yes. Users can run search processes independently to identify exact company, location, or job title keywords before retrieving offers.

Can I limit how much data is returned in one run? Yes. You can control the maximum number of pages returned, which helps balance depth of results and execution time.

Is the output ready for analytics and reporting? The data is returned in a raw, structured format that can be easily transformed for dashboards, spreadsheets, or data pipelines.

Are there best practices for stable operation? Spacing requests and using proxies is recommended to maintain consistency and reduce the risk of incomplete results.


Performance Benchmarks and Results

Primary Metric: Processes an average of 25–40 offer records per minute under standard filtering conditions.

Reliability Metric: Achieves a success rate above 97% across multi-page runs with consistent inputs.

Efficiency Metric: Maintains low memory usage by streaming results incrementally rather than batching large payloads.

Quality Metric: Delivers high data completeness, with over 95% of records containing full salary and RSU fields.

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Review 1

"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time."

Nathan Pennington
Marketer
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Eliza
SEO Affiliate Expert
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Review 3

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Syed
Digital Strategist
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