Workforce management platform for scheduling, timesheets, and shift operations
Deputy operates a workforce management platform handling employee scheduling, timesheets, tasking, and workplace communication. The tech stack reflects a modern SaaS architecture—React/Vue/Nuxt frontend, Go backend, gRPC/Protocol Buffers for service communication, Databricks/Delta Lake for analytics—paired with a sales and marketing infrastructure (Salesforce, Marketo, Customer.io, Segment). Hiring acceleration is engineering-heavy (11 open roles vs. 2 in sales), with seniority skewed toward senior and lead positions, suggesting both product complexity and internal scale challenges.
Deputy is a privately held Australian company founded in 2008, headquartered in Sydney. The platform manages shift scheduling, time tracking, task assignment, and team communication for mid-market and enterprise customers across 100+ countries and 245 industries. Over 500 million shifts have been scheduled through the system. The product integrates payroll systems and compliance features; active projects show focus on enterprise customization, payroll implementation, scalability improvements, and AI governance. Current pain points center on platform reliability at scale, reducing reactive support workload, and tooling gaps for frontline and shift workers.
Frontend: React, Vue, Nuxt, Next.js. Backend: Go, PHP, gRPC, Protocol Buffers. Data: Databricks, Delta Lake. Marketing/sales: Salesforce, Marketo, Customer.io, Segment. Design/content: Figma, Storyblok, Contentful. Monitoring: Datadog. CDN: Cloudflare Pages.
Yes. 11 of 24 active roles are engineering-focused, with positions split across senior (10 total headcount) and lead (9 total headcount) levels. All hiring is currently in Australia.
Enterprise customization and payroll implementation, marketing stack consolidation, AI governance, scalability improvements to reduce reactive workload, billing integration with Salesforce, and revenue analytics dashboards.
Deputy's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.