Aptible’s has an ambitious mission and strategy that we cannot achieve without a highly talented and motivated team.
We’ve always endeavored to attract and retain the best possible team members. We are writing down our philosophies on Talent Density and Compensation to help current and prospective team members understand how we think about our team, and how we compensate it.
We won’t achieve our mission, or really anything of any importance, without a highly talented and motivated team. We seek to attract and retain the best possible team members. We look for team members who embody our values, who will drive Aptible’s success, and will be great colleagues to collaborate with.
Netflix describes the concept of creating a team filled with talented people as creating “talent density.” Aptible has adopted this language because it provides convenient shorthand for how we seek to build our team. High talent density companies:
And, we believe, high talent density companies are more likely to create and deliver more valuable products to a wider customer base than those with average or low talent density.
We have lots of ideas about how to continuously improve our talent density, but with just over a dozen employees and needing to significantly grow our team quickly, the most important thing to focus on is attracting (and retaining) the highest talent individuals.
We believe that having a transparent and highly competitive compensation philosophy is the only way we’ll be able to attract great team members and retain them.
Aptible’s compensation philosophy is designed to be simple, transparent, and an effective tool for creating a high talent density team. We update our compensation philosophy as the needs of the business change. Our compensation process is based on industry-standard data sources, and seeks to ensure we are always paying our employees a highly competitive market rate at all times.
Compensation at Aptible consists of:
Aptible’s goal is that 100% of our employees’ total compensation is in line with 90th percentile for their personal market and Aptible’s market.
For most employees, Base Salary at Aptible is equivalent to Total Cash Compensation according to benchmarks. Save for a few select roles, Aptible does not use bonus compensation based on performance, opting instead to reward employees with generous top of market salaries in exchange for the expectation that every employee will make significant contributions to Aptible’s success.
For Aptible, personal market means:
Option Impact and Pave levels
When there is no clear comparable job title in the data set (e.g. Developer Relations) or sample sizes are small, we may average together data from more than one job title (for DevRel: Software Engineering and Marketing).
Aptible competes the entire market of software companies for talent. This is especially true for software engineering. As a result, for individual contributor roles Aptible benchmarks compensation against all companies.
Executive roles are a bit different. Because Aptible is still small (but growing fast), it’s very different to be the CTO of Aptible vs. being the CTO at Netflix. For that reason, we pay executives using comparables based on companies like Aptible. For Executives, we draw data from:
We may update the definition of comparable companies as Aptible grows.
Aptible uses Option Impact and Pave. We may adopt new data sources in the future.
For every role, we generally select one source. After reviewing the data, it sometimes become obvious that one source is more relevant than another due to one source having poor sample sizes or discrepancies in data; at those times, it’s obvious which source to select. If necessary, we will search for other data to help inform whether Pave or Option Impact.
As of the last teamwide compensation review, January 14, 2022, we’ve selected the following sources for individual contributor roles:
Job Family | Primary Source | Market | Levels | Rationale |
---|---|---|---|---|
Software Engineer | Pave | All companies | 1-6 | Pave is more up-to-date with rapidly changing market. We are competing in one big market for talent. |
Customer Success | Pave | All companies | 2-4 | Pave is more up-to-date with rapidly changing market. We are competing in one big market for talent. Levels after L4 are likely when someone moves into a manager/director role. |
Business Operations | Pave | All companies | 2-5 | Pave is more up-to-date with rapidly changing market. We are competing in one big market for talent. Levels after L5 are likely when someone moves into a manager/director role. |
Finance | Pave | All companies | 2-4 | Pave is more up-to-date with rapidly changing market. We are competing in one big market for talent. Levels after L4 are likely when someone moves into a manager/director role. |
Technical Support | Pave (Customer Service & Support) | All companies | 1-4 | Pave is more up-to-date with rapidly changing market. We are competing in one big market for talent. Levels after L4 are likely when someone moves into a manager/director role. |
Customer Reliability Engineering | Pave (Technical Support Engineer) | All companies | 1-4 | Pave is more up-to-date with rapidly changing market. We are competing in one big market for talent. Levels after L4 are likely when someone moves into a manager/director role. |
Product Management | Pave | All companies | 2-5 | Pave is more up-to-date with rapidly changing market. We are competing in one big market for talent. Levels after L5 are likely when someone moves into a manager/director role. |
We perform similar analysis for Managers through C-Level roles to determine appropriate compensation.
Each of the following trigger a compensation review:
If employees believe the market salary for their role has changed, they should collect their own compensation data (e.g. comparable company job descriptions with comp data, offers, etc.) to share with their manager and request a compensation review. However, since we target 90th percentile, we generally rely on data sources with larger samples sizes, we can’t promise to match every new input. That said, new data points will be helpful in verifying our data sources.