Lead, Production Resilience & Chaos Testing

Affirm, Inc.

Richmond (VA)

Remote

USD 204,000 - 264,000

Full time

2 days ago
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Benefits offered by this job

100% premium coverage for employees &/
Dependent coverage on health insurance
Monthly stipends for health, wellness,
ESPP stock purchase plan

Job summary

Affim is seeking an Engineering Manager to lead our Resilience Engineering team, driving production load testing and chaos engineering to ensure safe, reliable production systems. You will mentor engineers, build platforms for safe experimentation, and partner across teams to embed resilience in the SDLC.

You will shape the vision for resilience practices, govern safeguards, and enable scalable tooling with strong observability.

Qualifications

  • Proven experience leading engineering teams in reliability or distributed systems.
  • Hands-on with production load testing or chaos engineering.
  • Experience with cloud-native environments (AWS, Kubernetes).
  • Strong understanding of failure modes in distributed systems.

Responsibilities

  • Define and drive resilience engineering strategy and vision.
  • Lead engineers building platforms for safe production testing and chaos experiments.
  • Partner with infrastructure, product and security leadership to embed resilience validation in lifecycle.
  • Establish safeguards, isolation boundaries, and automated rollback mechanisms.

Skills

Leadership
Communication
Problem solving
Cross-team collaboration

Education

Bachelor’s degree or equivalent in related field

Tools

AWS
Kubernetes
Chaos engineering tools (Gremlin/Harness)
Python
Java

Job description

Affim is seeking an Engineering Manager to lead our Resilience Engineering team, driving production load testing and chaos engineering to ensure safe, reliable production systems. You will mentor engineers, build platforms for safe experimentation, and partner across teams to embed resilience in the SDLC.

You will shape the vision for resilience practices, govern safeguards, and enable scalable tooling with strong observability.

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