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.

---

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	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

---