---
title: "Job Mapping"
description: "Job mapping connects your internal jobs to market data from survey providers, enabling accurate benchmarking. This guide covers how to map job ladders, levels, and pay zones to survey data."
canonical_url: "https://support.pave.com/articles/job-mapping-57e5shgMRC"
md_url: "https://support.pave.com/articles/job-mapping-57e5shgMRC.md"
---
# Job Mapping

Job mapping connects your internal jobs to market data from survey providers, enabling accurate benchmarking. This guide covers how to map job ladders, levels, and pay zones to survey data.



---

## What is Job Mapping?

**Job mapping** is the process of associating your company's jobs with equivalent roles in survey data.

Mapping data sources to jobs is a key step in the Market Pricing process that enables you to efficiently match your job architecture in Pave to your market data for competitive compensation benchmarking.

**Example:**

```
Your job: Data Engineering - IC, Rank 3
Maps to McLagan: ADB (Business Intelligence/Analytics), Weight 1
Maps to Mercer: ADB (Business Intelligence/Analytics), Weight 1
Maps to Pave: Software Engineering (Generalist), Weight 1
```

Once mapped, your job pulls market data from those survey positions for benchmarking.

 ![](https://support.pave.com/api/attachments.redirect?id=53fb4f32-e453-49e5-93f8-055368b9fb09 " =2710x1906")


---

## How Mapping Works

Rather than mapping data sources directly to compensation bands (which can result in thousands of repetitive mappings and repeat work), you map the individual components of each job once (ladder, level, pay zone) and selectively use overrides for any exceptions.

**The three-step mapping process:**


1. **Map job ladders, levels, and pay zones** - Map each component of your job architecture to survey data sources
2. **Set weights and logic** - Define how your data sources should be applied and weighted when generating benchmarks (see **Data Rules** article)
3. **Generate benchmarks** - Generate the benchmarks for each job and review your data coverage across your job architecture and pay types


---

## Prerequisites

Before mapping:

* **Set up your job architecture** - Your functions, families, ladders, and ranks must be defined
* **Create your data sources** - Upload or connect survey data sources (Radford, Mercer, Pave Data, etc.)


---

## Core Concepts

### Benchmark Inputs

In the Benchmarking tab, you'll see benchmark completion status:

**Benchmark inputs card:**

* **Data sources**: 10
* **Jobs mapped**: 1,512
* **28 unmapped jobs** (warning indicator)

Click **View sources** to see your data sources list.
Click **View mappings** to access the mapping interface.

### 3 Core Entities

When mapping job ladders, job levels, and pay zones, there are **3 core entities** that can be mapped in each step:

**1. Job Architecture**
The first few columns reference the job architecture configured in Pave. Values in these columns should match the display values from your job architecture (ladders, levels, and pay zones).

**2. Pave Data Sources**
These columns reference the Pave data catalog, which is used to map any Pave data sources (aka data cuts) that have been created.

**3. Survey Data Sources**
These columns reference the parsed values from your survey data sources, which is used to map your survey data source job codes/locations.

### Methods to Create and Update Mappings

For each mapping step, there are **two methods** to create and update your mappings:

**1. (Recommended) CSV upload** - Easiest for bulk actions and first-time setup.

**2. In-app** - Good for incremental updates.


---

## Map Ladders

Map your job ladders to survey job families.

### Access Ladder Mapping


1. Go to **Market Pricing**
2. Open your band set
3. Click **Benchmarking** tab
4. Click **View mappings**
5. Select **Ladders** tab

### Ladder Mapping View

The Ladders tab shows a table with:

**Left sidebar:**

* **Ladders** (selected, with warning icon if unmapped)
* **Levels**
* **Pay Zones**
* **Overrides**

**Header:**

* Description: "Match your ladder names to data sources so Pave can pull the right benchmarks"
* **Sort** button
* **Show unmapped only** toggle
* **Filter** button
* **Search** field: "Search 49 job families"
* **Download CSV** button
* **Upload CSV** button
* **Edit** button

**Table columns:**

| Function | Family | Ladder | Ladder Rank | Label (McLagan) | Weight | Label (Mercer Comptryx) | Weight | Label (Pave) | Weight |
|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|
| Engineering | Data Science | Data Science - Manager | - | - | - | ADB | 1 | Business Inte... | 1 |
| Engineering | Software Engineering | Software Engineering - IC | - | - | - | RSS | 1 | Software Eng... | 1 |
| Engineering | Data Science | Data Science - IC | - | - | - | ADB | 1 | Business Inte... | 1 |

**Empty state:** "No data available yet" when no mappings exist

### Column Configuration for Ladders

**Job Architecture Columns:**

| Column | Description | Example |
|:---|:---|:---|
| Function | Required. The display Function from your job architecture | Engineering |
| Family | Required. The display Family from your job architecture | Data Science |
| Ladder | Required. The display Ladder from your job architecture | Data Science - IC |

Pave automatically creates a row for every distinct Function + Family + Ladder from your job architecture.

**Pave Data Source Columns:**

| Column | Description | Example |
|:---|:---|:---|
| Pave Label | The family from Pave's job catalog | Software Engineering (Generalist) |
| Pave Weight | The weight that Pave data sources should influence benchmarks. | 1 |

**Survey Data Source Columns:**

Survey data source columns are dynamically created based on the distinct set of Survey providers in your survey data sources list. Each distinct Survey provider creates:

| Column | Description | Example |
|:---|:---|:---|
| {Survey provider} Label (ex: McLagan Label) | The job code family prefix for this Survey provider | ADB |
| {Survey provider} Weight (ex: McLagan Weight) | The weight for this survey. | 1 |

### Map Ladders with CSV Upload


1. Click **Download CSV** to get a CSV with all mapping rows
2. Open the CSV and fill or update the Pave data source and Survey data source columns with the relevant mappings and weights for each ladder
3. Save the CSV, then click **Upload CSV** in Pave
4. Click **Upload Mappings** and select your CSV
5. Map the columns from your CSV to the correct Job architecture, Pave data source, and survey data source columns
   * Pave will attempt to auto-map your CSV to the correct columns
   * If needed, you can map a survey data source column by:

   
   1. Click the Template column dropdown → **Add new**
   2. Enter the Column name in the expected format for the Survey provider
   3. Choose the relevant Data type as either `Label` or `Weight`
   4. Click **Add**
6. Once columns are mapped, click to proceed
7. Review the results and finalize your ladder mappings
   * Pave will validate your mapped values, flagging any issues or errors before finishing the upload
   * Any row that references a Function + Family + Ladder that doesn't exist in your job architecture will throw an error
   * You may either resolve issues by editing the cells directly in-app (double click on cell) or updating then re-uploading the CSV
8. After reviewing, click to save your new mappings

**Note:** Importing replaces all existing values in the mapping table.

### Map Ladders In-App

You can make adjustments to your ladder mappings directly in-app. This is useful for incremental updates.


1. Click **Edit**
2. Edit, duplicate, or delete any mappings
   * To modify Job architecture columns (including adding a completely new ladder), use the CSV upload method
3. Click **Save**

**Example mapping:**

```
Ladder: Data Science - IC
  → McLagan: ADB (Business Intelligence/Analytics, Weight: 1)
  → Mercer Comptryx: ADB (Weight: 1)
  → Pave: Business Intelligence/Analytics (Weight: 1)
```


---

## Map Levels

Map your job levels to survey seniority levels.

### Access Level Mapping


1. Go to **Benchmarking** tab
2. Click **View mappings**
3. Select **Levels** tab

### Column Configuration for Levels

**Job Architecture Columns:**

| Column | Description | Example |
|:---|:---|:---|
| Display Level | Required. The Display Level from your job architecture | P3, Senior, IC3 |

**Pave Data Source Columns:**

| Column | Description | Example |
|:---|:---|:---|
| Pave Level | The level from Pave's job catalog | IC3, P3 |
| Pave Weight | The weight that Pave data sources should influence benchmarks.  | 1 |

**Survey Data Source Columns:**

| Column | Description | Example |
|:---|:---|:---|
| {Survey provider} Job Code Level Suffix | The job code level suffix for this Survey provider | P3, .03 |
| {Survey provider} Weight | Optional. The weight for this survey.  | 1 |

### Map Levels with CSV or In-App

Follow the same process as for ladders (CSV upload or in-app editing).


---

## Map Pay Zones

Map your pay zones to survey locations.

### Access Pay Zone Mapping


1. Go to **Benchmarking** tab
2. Click **View mappings**
3. Select **Pay Zones** tab

### Column Configuration for Pay Zones

**Job Architecture Columns:**

| Column | Description | Example |
|:---|:---|:---|
| Pay Zone Name | Required. The Pay Zone Name from your pay zones configuration | San Francisco, Remote - Tier 1 |

**Pave Data Source Columns:**

| Column | Description | Example |
|:---|:---|:---|
| Pave Location | The location from Pave's data catalog | US - United States Tier 1 |
| Pave Weight | The weight that Pave data sources should influence benchmarks.  | 1 |

**Survey Data Source Columns:**

| Column | Description | Example |
|:---|:---|:---|
| {Survey provider} Location | The location for this Survey provider | US - United States, SF - San Francisco Bay Area |
| {Survey provider} Weight | The weight for this survey.  | 1 |

### Map Pay Zones with CSV or In-App

Follow the same process as for ladders (CSV upload or in-app editing).


---

## Apply Overrides

After mapping ladders, levels, and pay zones, you can set overrides to add exceptions for individual jobs.

### Access Overrides


1. Go to **Benchmarking** tab
2. Click **View mappings**
3. Select **Overrides** tab

### When to Use Overrides

Overrides allow you to map a specific job directly to a survey job code when the mapping doesn't align with your standard ladder and level mappings.

**Use when:**

* Specialized role doesn't fit standard mapping
* Job title inflated/deflated vs. actual scope
* Unique position in your company
* Need to map one specific job differently from the rest of its ladder

### Column Configuration for Overrides

**Job Architecture Columns:**

| Column | Description | Example |
|:---|:---|:---|
| Function | Required. The display Function from your job architecture | Engineering |
| Family | Required. The display Family from your job architecture | Data Engineering |
| Ladder | Required. The display Ladder from your job architecture | Data Engineering - IC |
| Ladder Rank | Required. The Ladder Rank from your job architecture | 3 |

**Pave Data Source Columns:**

| Column | Description | Example |
|:---|:---|:---|
| Pave Job Code |  ![CleanShot 2026-08-03 at 09.03.20@2x.png](https://support.pave.com/api/attachments.redirect?id=58109038-d536-492f-a7f3-ce6a755b37b1)The job family and level from Pave's data catalog. Format: `Family [space] Level` | Data Engineering P3 |
| Pave Weight | The weight for Pave data sources. | 1 |

### Creating Overrides

Use CSV upload or in-app editing to create overrides for specific jobs.

**Example override:**

```
Job: Engineering - Data Engineering - IC - Rank 3
Standard mapping would use:
  Ladder: ADB (Business Intelligence/Analytics)
  Level: P3

Override to:
  Survey Job Code: EN.SWDE.P4 (mapping to Software Development at higher level)
  Reason: This role has broader scope than typical Data Engineering P3
```


---

## Related Articles

* **Manage Data Sources**: Setting up surveys before mapping
* **Data Rules**: Configuring how mapped data is used
* **Reviewing Benchmarks**: Seeing mapping results


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