Senior Staff Site Reliability Engineer

Staff Site Reliability Engineer (Hybrid - San Jose, CA) 

The Role

We are looking for a Staff Site Reliability Engineer to join our SRE organization as a technical specialist. In this role, you will focus on engineering the "glue" that makes our systems resilient—building custom internal tools, refining our observability stack, and ensuring our SLO/SLI frameworks are technically sound. You will work alongside our existing SRE teams to convert manual operational tasks into automated, programmable infrastructure.

Key Responsibilities

• Standardize SRE Procedures: Design and implement consistent technical procedures for incident response, error budget tracking, and production readiness across our service catalog.
• Engineer SLOs & SLIs: Technically instrument our services to capture precise SLIs. You will be responsible for the backend logic that calculates Error Budgets and triggers automated alerts based on burn rates.
• Build Special Purpose Tooling: Write production-grade code (Go, Python, etc.) to build internal tools that solve specific infrastructure gaps, such as custom Kubernetes operators, automated remediation scripts, or deployment safety gates.
• Executive & Operational Dashboards: Create a unified observability layer. This includes deep-dive Grafana/Datadog dashboards for real-time debugging and high-level, aggregate views for executive stakeholders to monitor SLA compliance.
• Toil Reduction: Identify repetitive operational tasks across the group and build the automation necessary to eliminate them.
• Collaborative Engineering: Work within the SRE group to provide expert-level code reviews for infrastructure changes and contribute to the collective codebase of our internal platforms.

Technical Requirements

• Experience: 8+ years in SRE, Production Engineering, or high-scale DevOps environments.
• Advanced Programming: Strong software engineering fundamentals. You should be as comfortable writing a microservice or a custom API as you are configuring a load balancer.
• Metric Logic: Deep understanding of how to derive meaningful SLIs from complex distributed systems (latency percentiles, success rates, etc.).
• The Stack:
• Orchestration: Expert-level Kubernetes (EKS, GKE, or self-managed).
• IaC: Advanced Terraform or Pulumi.
• Observability: Mastery of the "Three Pillars" (Logs, Metrics, Traces) using tools like Prometheus, Jaeger, or Datadog.
• Communication: Ability to distill complex system health data into clear, actionable reports for both engineers and business leadership.

Please note that this job description is intended to provide a general overview of the position and does not include an exhaustive list of responsibilities and qualifications

At Archer we aim to attract, retain, and motivate talent that possess the skills and leadership necessary to grow our business. We drive a pay-for-performance culture and reward performance that supports the Company’s business strategy. For this position we are targeting a base pay between 133,400 - 200,000. Actual compensation offered will be determined by factors such as job-related knowledge, skills, and experience.

Archer is proud to be an Equal Opportunity employer committed to diversity and inclusivity in the workplace. All aspects of employment are decided on the basis of merit, qualifications, and business needs. We do not discriminate based upon race, color, religion, sex, sexual orientation, age, national origin, disability status, protected veteran status, gender identity or any other characteristic protected by federal, state or local laws.

(This position is not eligible for visa sponsorship)

 

 

Archer is committed to working with and providing reasonable accommodations to job applicants with physical or mental disabilities, and those with sincerely held religious beliefs. Applicants who may require reasonable accommodation for any part of the application or hiring process should provide their name and contact information to Archer’s People Team at people@archer.com. Reasonable accommodations will be determined on a case-by-case basis.