Senior Platform Engineer

present of AI; Palo Alto (CA); On-site; USD 180,000 - 230,000; Full time

Job summary

Endor Labs seeks a Senior Data Platform Engineer in Palo Alto to own the data layer behind a security platform. You will run MongoDB and BigQuery from initial setup through daily operations, while building cloud infrastructure on Azure and Google Cloud to handle very large event volumes.

A strong candidate has hands-on production MongoDB work with sharding and replica sets, plus site reliability or platform engineering experience with on-call duty and incident response.

Qualifications

• Hands-on production MongoDB work including sharding and replica sets.
• Site reliability/platform engineering with on-call duty and incident response.
• Kubernetes, Terraform-style infrastructure as code, and Prometheus/Grafana observability.

Responsibilities

• Owns the data layer behind a security platform, running MongoDB and BigQuery from setup through daily operations.
• Builds cloud infrastructure on Azure and Google Cloud handling large event volumes.

Skills

MongoDB sharding

Terraform workflows

GitOps

Prometheus Grafana observability

On-call incident response

Tools

MongoDB

Kubernetes

Terraform

Prometheus

Grafana

Azure

Google Cloud

BigQuery

Job description

Engineering · Palo Alto, CA · Senior level

This role owns the data layer behind a security platform, running MongoDB and BigQuery from first setup through daily operations, and building cloud infrastructure on Azure and Google Cloud that handles very large event volumes. A strong candidate has years of hands on production MongoDB work including sharding and replica sets, plus site reliability or platform engineering experience with on call duty and incident response. Kubernetes, Terraform style infrastructure as code, and Prometheus and Grafana observability round out the core expectations.

How it fits the AI era

The platform exists because AI agents write code faster than people can review it, so the infrastructure must keep up with a rising tide of security events. Much of the routine provisioning and monitoring can be automated, but shard key choices, scaling tradeoffs, and incident judgment still rest on human experience. The role sits close to the AI coding wave without requiring the engineer to build models.

How it builds AI experience

Working here means operating infrastructure that ingests and processes security findings about AI generated code, so the engineer gains exposure to how agentic security tooling behaves at scale. Skills build through running telemetry and data pipelines that surface vulnerabilities in code and dependencies, and through learning how agent actions are tracked outside the model. The posting does not ask for direct AI model work, so the growth comes from the surrounding data and platform problems.

Worth showing

• MongoDB sharding at scale
• Terraform and GitOps workflows
• Prometheus and Grafana observability
• On call incident response

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

Per the company, Endor Labs provides an agentic application security platform that secures AI-generated code, open source dependencies, and containers.