Salary Benchmark Tool

What is this?

The salary lookup tool (salary_lookup.py) lets you benchmark company salaries against a baseline from your own data. It's used during the /apply workflow to show how a company's compensation compares to market rates.

This tool is optional. If you don't have salary data, the salary step is simply skipped during /apply.

How it works

The tool reads a salary_data.json file in the repo root containing company salary benchmarks. It uses fuzzy matching to find companies by name, handling Danish/Nordic characters, legal suffixes (A/S, ApS), and common spelling variations.

The data format supports any index-based or absolute salary data. For example:

Index 100 = median salary, higher is better
Absolute salary values in your currency
Any custom metric you want to track

Data format

The tool expects salary_data.json with this structure:

{
  "metadata": {
    "source": "My Union Statistics 2025",
    "index_baseline": 100,
    "index_label": "Index",
    "baseline_description": "Index 100 = median salary for private sector"
  },
  "companies": [
    {
      "company": "Novo Nordisk A/S",
      "city": "Bagsværd",
      "categories": {
        "all_employees": { "count": 500, "index": 108.5 },
        "engineering": { "count": 120, "index": 112.3 }
      }
    },
    {
      "company": "Ørsted A/S",
      "city": "Fredericia",
      "categories": {
        "all_employees": { "count": 200, "index": 105.2 }
      }
    }
  ]
}

Fields

metadata.source: Where the data comes from (for reference)
metadata.index_baseline: The baseline value (e.g., 100 for index-based data)
metadata.index_label: Label for the index column in output
metadata.baseline_description: Human-readable explanation of the baseline
companies[].company: Company name (required)
companies[].city: City/location (optional, used for filtering)
companies[].categories: Named salary categories, each with count and/or index

Setup options

Option A: Create salary_data.json manually

Create the file by hand with data from any source: union statistics, Glassdoor, salary surveys, networking, or personal research.

Option B: Convert from Excel

If you have salary data in an Excel file:

pip install openpyxl
python3 tools/convert_salary_excel.py path/to/salary-data.xlsx \
  --source "My Salary Data 2025" \
  --baseline 100 \
  --baseline-desc "Index 100 = median salary"

On Windows, use py if that is how Python is exposed on your PATH. If your system uses python instead of python3, substitute that in the examples.

The converter auto-detects the Excel layout:

Looks for a "Company"/"Firma" column and an optional "City"/"By" column
Treats remaining columns as salary data (auto-pairs count/index columns)

Option C: Build from research

Start with an empty template and add companies as you research them:

{
  "metadata": {
    "source": "Personal research",
    "index_baseline": 0,
    "index_label": "Monthly salary (DKK)",
    "baseline_description": "Approximate monthly salary before tax"
  },
  "companies": [
    {
      "company": "Example Corp",
      "city": "Copenhagen",
      "categories": {
        "entry_level": { "index": 42000 },
        "senior": { "index": 55000 }
      }
    }
  ]
}

Usage

python3 salary_lookup.py "Novo Nordisk"
python3 salary_lookup.py "Ørsted" --city "Fredericia"
python3 salary_lookup.py "COWI" --json
python3 salary_lookup.py --list-all

Important notes

The data file (salary_data.json) is excluded from git (see .gitignore). Your salary data may be proprietary or confidential.
If the data file is missing, salary_lookup.py exits with a helpful error message and the /apply workflow skips the salary benchmark step.
The fuzzy matcher handles Danish company name variations: legal suffixes, Nordic characters, anglicized spellings, and partial matches.

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