Sr. Staff Platform/Data Reliability Engineer, Databricks (R5537)

About this role

Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube.

### Job Description:

The Sr. Staff Platform / Data Reliability Engineer is responsible for making Databricks a reliable, secure, scalable, and operationally mature platform for enterprise use. This role focuses on the platform itself rather than domain-specific business modeling, ensuring the lakehouse can support multiple domains, regulated data handling, and a growing set of production workloads without becoming fragile, expensive, or hard to govern.

This role partners closely with the Senior Staff Data Engineer, who focuses more heavily on ingestion and medallion implementation patterns, and with the existing Cloud & Infrastructure team, which owns cloud accounts, networking, and foundational infrastructure. The Platform / Data Reliability Engineer owns the Databricks operational layer that sits above that foundation: reliability, observability, deployment standards, compute and job policies, and platform enablement patterns for internal users.

### What you'll do:

• Own operational excellence for the Databricks platform, including monitoring, alerting, observability, incident response support, and production runbook patterns for data jobs and platform services.
• Define and maintain CI/CD and promotion standards for Databricks assets, including workflows, jobs, notebooks, code packages, infrastructure configuration, and environment promotion from dev to prod.
• Design and maintain platform standards for job orchestration, cluster and co...