Diversity data methodology
All data was voluntarily provided by Atlassians, and taken from our Human Resources Information System (HRIS) or employee engagement survey platform. This provided the most complete data set on which to base our analyses.
All data is representative of full-time Atlassians. The data excludes contractors and interns. Team-level statistics exclude "teams" of one.
All data is accurate as of June 30, 2018. 12-month hiring rate refers to the percentage of Atlassians who started employment between July 1, 2017 and June 30, 2018.
Team: Teams were defined using the "Team" classification within HRIS (e.g., "Talent Acquisition" or "JIRA Product Marketing"). We chose to rely on this definition because we assume, on average, that Atlassians work more closely with similarly-classified Atlassians. We also chose this definition because it provides us with the most standardized unit of analysis and ensures the replicability of our analyses over time.
Female/Women: This category describes all full-time Atlassians who have identified as female within our HRIS. We have coded gender as binary due to data limitations and for simplicity, although we recognize that gender exists on a spectrum and these categorizations may not accurately represent all Atlassians.
Total workforce: Percentage of full-time Atlassians who have self-identified as female.
Technical: All development, product management, and design roles on our Software teams as well as non-Software roles where coding is a primary job requirement (e.g., IT development, Technical Support).
Leadership: All Atlassians with direct reports and senior-level individual contributors.
Race: All race data is for full-time U.S. Atlassians only, as race and ethnicity data is not available in our other locations. Our chosen categories are modeled off of the U.S. government's Equal Employment Opportunity Commission (EEOC) ontology, although we acknowledge these are imperfect categorizations.
Age: Age represents the computed value based on each Atlassian's date of birth.
International: This reflects the number of unique countries of origin for full-time employees by office.
Belonging: We measured belonging via our biannual employee engagement survey, specifically looking at levels of agreement with “I feel like I belong on my team.” We chose the most marginalized group within a particular category (e.g., women, Black employees) when showing relative levels of belonging.
Our first priority when undertaking this analysis was to decide what constitutes a "team". The traditional definition is, "a number of persons associated in some joint action." We chose to use this definition, and proxy it within our data using the method described in "Definitions" above.
The next step was to decide the schema by which we would group our teams. We chose an ontology that aligns with the kinds of teams we see our customers are on. Here are the kinds of functions within each of our departments listed in the report (in alphabetical order):
- Customer Support: Customers for Life, Technical Support, Account Management, Advocates, etc.
- Finance: Analytics, Planning, Business Development, Tax, etc.
- HR: HR, Talent Acquisition, Experience & Real Estate
- IT: Administration, Business Systems, Security, Workplace Technology, etc.
- Legal: Legal, Risk & Compliance, Corporate Development, etc.
- Marketing: Corporate Branding, Communications, Marketing, etc.
- Software Development: Engineering, Design, Product Management, Infrastructure, Security, etc.
Average Team Diversity
In order to look at the average team within each department, we first found the total representation of each demographic group within each team. We then took the averages of the representation as a percentage to find the average representation per team per department.
Analyses for race data excludes teams with one or fewer U.S.-based team members. The percentages shown represent only data for US-based team members.
Diversity Distributions Across Teams
We looked at each individual team within a department. The summary percentage in this table represents the percentage of teams within that department that share the characteristic described in the header.