Sr Staff Engineer, Data Infrastructure

Responsibilities

• Data Plane Ownership: Architect and manage the lifecycle of high-throughput data tools including Trino, Ray, and JupyterHub on Kubernetes.
• GitOps & Automation: Drive a "zero-manual-touch" philosophy using ArgoCD and Terraform to manage complex, stateful data environments.
• Observability at Scale: Build high-cardinality monitoring systems using VictoriaMetrics and Vector to track pipeline health, data ingestion rates, and system performance.
• ML Lifecycle Support: Maintain and optimize MLflow for model tracking, ensuring it integrates deeply with our compute and storage layers.
• Engineering Sovereignty: As a self-starter, you will identify performance bottlenecks in data processing and proactively implement infrastructure-level optimizations.
• Reliability: Participate in on-call rotations for the data stack, treating "data downtime" with the same urgency as a site outage.

Qualifications

• The "Data-Aware" Engineer: You understand that scaling a database or a Ray cluster is different from scaling a stateless API. You know how to handle persistent volumes and data gravity.
• Senior Leadership: You’ve spent time in the trenches. You’ve been on-call for 2:00 AM outages and have built the automation to ensure those outages never happen twice.
• Tooling Polyglot: You don't just use tools; you contribute to them. You are comfortable writing Go or Python to extend Kubernetes Operators or automate data workflows.
• Self-Directed: You thrive in ambiguity. You can take a high-level requirement ("Make Trino faster") and turn it into a multi-week infrastructure roadmap.