A hiring platform can show open jobs without changing who can reach them. A training course can give a certificate without changing the workplace waiting on the other side. Brazil’s PretaLab takes a wider view of Black women in technology: it links professional networks, education, research and services for companies. Its own public materials are most persuasive when they acknowledge how little reliable data the sector has collected about the people it claims to need.
PretaLab describes itself as a platform connecting Black women who work in or want to enter technology. The site offers free technical training for Black and Indigenous women and a searchable network of professionals. It also offers services to employers, explaining that raising visibility is insufficient if companies are unprepared to receive and retain those workers. That is a concrete institutional diagnosis. The gap is not only a shortage of applicants or skills; it is also a question of recruitment, workplace practice and professional continuity.
Its 2022 report explains why data cannot carry more weight than its method permits. PretaLab and ThoughtWorks interviewed 693 technology professionals in Brazil during 2018–19 for the #QuemCodaBr survey. The report explicitly says that sample is not statistically representative. It criticizes other industry data for combining Black and Indigenous women and for counting people employed by technology firms without isolating technical roles. Those distinctions are not pedantry. A company can announce a diversity number that improves when a nontechnical department hires, while its engineers and decision-makers remain much the same.
PretaLab’s work is therefore two connected projects. One is practical: courses, network building and employer engagement that may alter individual opportunities. The other is epistemic: insisting on better categories before the industry tells a triumphal story about itself. Neither project proves a nationwide change in employment. The platform’s own training count is a company-reported number, not an independent labor-market outcome. The value of the model is that it makes the route from preparation to hiring visible enough to be examined.
Coverage of Black Brazilian technologists often collapses into a single person who broke through. That frame misses the infrastructure around every career: colleagues who share information, instructors who teach usable tools, employers who decide whose experience counts, researchers who ask better questions. PretaLab puts those relationships in the foreground. The most rigorous reading of its work accepts its ambition and its limits together. A network can widen access, and a survey can reveal a problem; neither substitutes for sustained evidence of who gets technical authority inside the firms that shape Brazil’s digital future.