Staff Backend Engineer, Scheduling - Global Workforce Tech

Rippling

New York (NY)

On-site

USD 189,000 - 315,000

Full time

14 days+

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Job summary

Rippling is seeking a Staff Full Stack Software Engineer (Backend) to join the Scheduling team in New York. You will build scalable backend systems in Python/Django to power workforce management, with observable impact on global customers and time-critical payroll and attendance features.

This senior role emphasizes architecture, performance, and collaboration with product managers and designers, mentoring junior engineers, and shaping the direction of the Scheduling product as it expands

Qualifications

  • 8+ years in backend development with Python/Django or similar.
  • Frontend experience with React/JavaScript is a plus.
  • Strong CS fundamentals and scalable systems design.
  • Focus on end-user experience and product quality.
  • Comfortable in fast-paced, unstructured environments.

Responsibilities

  • Write scalable, maintainable backend code powering core scheduling workflows.
  • Deliver high-quality features quickly with balance of speed and safety.
  • Own architecture and drive product lifecycle from ideation to production.
  • Collaborate with product managers, designers, and teams to refine roadmaps.
  • Mentor junior engineers and promote engineering best practices.
  • Shape team culture, structure, and processes as an early team member.

Skills

8+ years backend experience
Python/Django
React/JavaScript
CS fundamentals & system design
User-focused product mindset
Entrepreneurial spirit

Job description

Rippling is seeking a Staff Full Stack Software Engineer (Backend) to join the Scheduling team in New York. You will build scalable backend systems in Python/Django to power workforce management, with observable impact on global customers and time-critical payroll and attendance features.

This senior role emphasizes architecture, performance, and collaboration with product managers and designers, mentoring junior engineers, and shaping the direction of the Scheduling product as it expands

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