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

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 Follow Shield AI on LinkedIn, X, Instagram, and YouTube.

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 compute policies, service principal usage, environment isolation, and production execution reliability.
• Establish reusable operational templates and enablement patterns for new domains onboarding to Databricks, including logging conventions, job tagging, metadata capture, and support handoff expectations.
• Partner with the Senior Data Engineer to ensure ingestion and medallion patterns are implemented in a way that is observable, recoverable, cost-aware, and secure in production.
• Work with the cloud/infrastructure team to align Databricks configuration and usage patterns with broader enterprise cloud standards, especially where commercial and future government-hosted environments are involved.
• Help enforce technical controls for data segregation, access boundaries, and operational compliance in a highly regulated environment.
• Track and improve platform health metrics such as job success rates, incident trends, data pipeline reliability, cost efficiency, and environment drift.
• Document platform standards, operational expectations, and support models so the Databricks platform can scale beyond a small founding team.
• Mentor internal engineers who are growing into platform responsibilities, helping expand Databricks operational knowledge within the team.

Required qualifications:

• 12+ years of relevant experience in data platform engineering, platform operations, site reliability engineering, or modern cloud data infrastructure.
• Hands-on experience with Databricks or a closely related cloud data platform in production environments.
• Experience designing or operating CI/CD, environment promotion, version control, and deployment automation for data platforms and pipelines.
• Strong understanding of platform operations concepts such as observability, monitoring, alerting, incident management, and reliability engineering.
• Experience with compute policy design, workload isolation, service principals, and secure production execution patterns on cloud data platforms.
• Ability to work effectively in a regulated or security-sensitive environment with strong expectations around access control, auditability, and operational discipline.
• Strong collaboration skills and comfort partnering with cloud/infrastructure, security, data engineering, and analytics stakeholders.

Preferred qualifications:

• Databricks certification and/or strong demonstrated expertise with Delta Lake, Unity Catalog, Workflows, and Databricks Asset Bundles.
• Experience with infrastructure-as-code and platform automation in enterprise environments.
• Experience supporting commercial and government or otherwise segregated environments with different compliance and access requirements.
• Experience in defense, aerospace, federal, or another regulated industry.