Director, Cloud Platform Engineering: Data & AI Cloud
Lincoln Motor Company
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Benefits offered by this job
Job summary
Ford Motor Company seeks a visionary Director of Cloud Platform Engineering to own strategy, architecture, and engineering of Ford's cloud database and analytics platforms on Google Cloud. Lead a global team, adopt IaC, and drive cost optimization and consolidation of SaaS databases.
anchored in DBaaS strategy for PDO workloads. The role emphasizes platform engineering as internal products, strong governance, and collaboration with SaaS vendors to advance agentic AI capabilities across the data
Qualifications
- 15+ years in software engineering, cloud architecture, and data platform services with leadership experience.
- Proven platform ownership of large-scale database and analytics environments on GCP.
- Strong hands-on experience with Infrastructure-as-Code and DevOps/SRE practices.
- Experience consolidating SaaS database products and managing costs at enterprise scale.
- Bachelor's degree required; advanced degree preferred.
Responsibilities
- Define and execute a multi-year platform engineering roadmap for cloud databases and analytics.
- Lead a global team of platform engineers, architects, and SREs.
- Drive IaC-driven provisioning, security, and observability across platforms.
- Own DBaaS strategy and architecture patterns for PDO workloads.
- Consolidate SaaS databases and optimize licensing and costs.
- Partner with vendors and internal teams to advance agentic AI data capabilities.
Skills
Education
Tools
Job description
Ford Motor Company seeks a visionary Director of Cloud Platform Engineering to own strategy, architecture, and engineering of Ford's cloud database and analytics platforms on Google Cloud. Lead a global team, adopt IaC, and drive cost optimization and consolidation of SaaS databases.
anchored in DBaaS strategy for PDO workloads. The role emphasizes platform engineering as internal products, strong governance, and collaboration with SaaS vendors to advance agentic AI capabilities across the data
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