Pave Job Catalog FAQs: New Job Families, Job Levels, Filters & More

Compensation professionals need job data that matches the complexity of every role—detailed where it matters for critical decisions, streamlined where excessive granularity just creates noise. You also need compensation data you can trust to be current and accurate when you're making those high-stakes offers and planning strategic initiatives.

With the recent improvements to Pave’s Market Data product, our updated job catalog delivers exactly that: precision-built classifications for scaling technology organizations, backed by real-time compensation data that keeps you ahead of market shifts. 

Here, we've compiled answers to some of the most common questions about the new catalog. Read on to explore how these changes can streamline your most critical compensation work.

What jobs families are in the new catalog?

We've doubled the size of our job catalog, expanding from ~110 job families to over 200, in addition to introducing more intuitive functions, family blends, and job descriptions to make it easier to find the right job. 

For example, our AI & ML Engineering family blend includes breakouts into Artificial Intelligence (AI) Engineering, Artificial Intelligence (AI) Research Scientist, Machine Learning (ML) Engineering, Machine Learning Operations (MLOps) Engineering, and Computer Vision Engineering.

We analyzed patterns in our dataset to ensure we only added jobs with meaningful distinctions between each other. The breadth of this expansion enables compensation professionals to benchmark pay for more roles with greater granularity.

It’s worth noting that it will take some time for every job family included in the catalog to populate with sufficient data. We’re constantly increasing the size of our dataset to provide more benchmarks, in addition to using new methods like Calculated Benchmarks to fill in any gaps in the compensation data.

What job levels and tracks are in the new job catalog?

With this update, Pave merged employee and executive benchmarks into a single catalog, making it easy to find the right job regardless of the level of seniority. This means you can easily find compensation data for an entry level software engineer, engineering manager, VP of Engineering, and CTO all from the same place.

The new job catalog includes the following job levels and tracks:

Executive

• C-level (E9)
• SVP (E8)
• VP (E7)

Management

• M2 (coming soon)
• M3
• M4
• M5
• M6

Professional

• P1
• P2
• P3
• P4
• P5
• P6
• P7 (coming soon)

Support

• S1
• S2
• S3

Note that not every track and level will be supported for every job family. This is intentional to ensure the job catalog represents jobs that actually exist in the market. 

How do I filter within the job catalog?

Improved filtering is a large component of this suite of Market Data enhancements. This includes a new, modern industry filter that expands our industry taxonomy to support customers in a broader range of industries. For example, you’ll be able to view salary benchmarking data for roles across market segments from Enterprise Technology to Financial Technology to Healthcare Technology.

In addition, we outlined industry definitions and participant lists to provide you with a better understanding of our industry groupings. 

Another important filtering update is to the company stage filter, which now allows customers to filter all aspects of Market Data by more granular company stages. These improved demographic filtering capabilities mean you can drill down by capital raised, headcount, revenue, and valuation in order to fine-tune how you benchmark pay.

Why did the data change from old to new job families?

If you’re an existing Pave user, you may have noticed some of the compensation data you reference may have changed up or down with the updates to the job catalog and filters. There are a few reasons for this:

1. The job catalog is more granular than before: Depending on the job, there may be new levels of granularity due to the fact that there are actually two roles paid differently within that job title. In these cases, if you want to find data that’s most similar to what you previously viewed, we recommend a blend. If you’re looking for data that’s more granular, we recommend reviewing the job catalog to determine the best match.
2. The filters have been updated: As mentioned previously, we’ve also updated some of our company stage filters and added an industry filter. If you’re utilizing a filter that’s slightly different than before, it can impact the benchmarks as companies pay differently across stages and industries. 
3. The methodology is improved: In addition to the above, we’ve also made improvements to our methodology for job matching and benchmark generation, which not only power the new catalog and features, but also improve the precision and accuracy of our benchmarks. This is something we have been and are continuing to invest in to provide customers with even greater reliability in Pave’s compensation data.

How is Pave’s Job Catalog different from other survey providers?

Pave’s job catalog provides detail where it matters and simplicity where it doesn’t. Compared to traditional survey providers, Pave is unique in a few ways:

1. Hyper-focused on technology companies: You’ll see more specific roles within the technology sector, with detailed engineering breakouts to provide accurate compensation data and benchmarks in critical, high-paying fields.
2. More intuitive structure: The catalog itself is easy to navigate because it mirrors the structure of most technology organizations.
3. Quick addition of new jobs: As new jobs are created in the market, Pave’s real-time dataset will be the first to report on them. With real-time connections to HRIS platforms, we’re able to add new jobs to the catalog in a matter of weeks, not months or years.
4. Constant evolution: As our dataset continues to grow and the market continues to evolve, our job catalog will adapt with it. We’ll continue to add new jobs and new industries, making it seamless for customers to adopt the latest trends. 

Where can I learn more?

To dive into the job families and functions available within Market Data, explore Pave’s job catalog on our website. If you’re ready to learn more about the full suite of enhancements and whether our Market Data product is a good fit for your organization, request a demo today.

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