Director, Cloud Platform Engineering

Ford Motor Company is transforming mobility through enterprise technology and advanced transportation solutions. The Director, Cloud Platform Engineering will lead the strategy, architecture, and engineering of Ford's cloud database and analytics platforms on Google Cloud, while overseeing DBaaS, infrastructure-as-code, platform optimization, and Agentic Data Cloud capabilities. The role also includes building global engineering teams, defining architecture standards, and enabling secure, scalable self-service data platforms.

Responsibilities:

• Serve as Ford's visionary platform engineering leader for cloud databases and analytics — setting a multi-year strategic roadmap that anticipates future AI, security, and scale requirements
• Instill a platform engineering mindset across the data estate — treating databases and analytics systems as internal products with self-service APIs, golden paths, paved roads, and clear SLOs rather than ticket-driven infrastructure
• Drive all platform services through an Infrastructure-as-Code (IaC) mindset — defining, provisioning, and managing database and analytics infrastructure as version-controlled, testable, and repeatable code rather than manual configuration
• Continue to deepen partnerships with cloud and SaaS vendors, co-building rich, differentiated capabilities within their platforms so Ford operates as a strategic technical partner rather than a passive consumer
• Build, mentor, and lead a global, high-performing team of platform engineers, architects, and SREs; foster a culture of innovation through hackathons, ideathons, and DevSecOps community events
• Architect and operate GCP-native database platforms (Cloud SQL, Spanner, AlloyDB, BigTable, Firestore) with consistent, reusable, IaC-driven platform engineering patterns across provisioning, security, and observability
• Own platform engineering for BigQuery and the broader analytics stack — building governed, self-service data pipelines and reusable analytics tooling for product and data science teams
• Build golden-path templates, service APIs, and IaC-based paved-road automation that let engineering teams provision and operate database and analytics resources safely and consistently at scale
• Own Ford's Cloud Database-as-a-Service (DBaaS) strategy, enabling self-service provisioning across GCP-native and SaaS database platforms with automated, golden-path IaC pipelines
• Define and evangelize database architecture patterns that guide Product Development Organizations (PDO) toward fit-for-purpose database selection — matching workload characteristics to the right relational, NoSQL, graph, or in-memory technology rather than defaulting to a single platform
• Partner with PDO application and engineering teams to embed these architecture patterns into design reviews, reference architectures, and reusable templates
• Lead enterprise-wide consolidation of SaaS database products (e.g., MongoDB, Neo4J, Redis), reducing platform sprawl, redundant licensing, and support overhead
• Own full lifecycle management of database, analytics, and SaaS platforms — versioning, patching, upgrade planning, end-of-life tracking, and decommissioning of unused resources — to proactively eliminate unnecessary and duplicate cost
• Own full lifecycle management of database, analytics, and SaaS platforms — versioning, patching, upgrade planning, end-of-life tracking, and decommissioning of unused resources — to proactively eliminate unnecessary and duplicate cost
• Own cost-optimization strategy and cloud-cost mindsets (rightsizing, usage governance, cost recommenders) across the data platform portfolio; manage vendor contracts and negotiate enterprise agreements
• Define and evangelize Ford's Agentic Data Cloud strategy, shifting the enterprise from a passive system of intelligence to an agent-centric system of action
• Provide application teams with standardized, reusable patterns for database MCP (Model Context Protocol) Servers, enabling AI agents to safely discover, reason over, and act on enterprise data
• Lead deployment of Knowledge Catalog capabilities for AI-context governance and data discoverability, and roll out Conversational Analytics patterns for natural-language data interaction across Ford's applications
• Define guardrails and human-in-the-loop controls aligned to Ford's Agentic AI Rubric; partner with Google product and engineering teams (GDIA, Enterprise Architecture) on POC design and service launch frameworks

Requirements:

• 15+ years in software engineering, cloud architecture, and data platform services, including significant leadership experience
• Proven platform engineering ownership of large-scale database and analytics environments on GCP (Cloud SQL, Spanner, AlloyDB, BigTable, Firestore, BigQuery)
• Strong hands-on experience with Infrastructure-as-Code tooling and practices, driving platform services through code-defined, version-controlled automation rather than manual provisioning
• Direct experience with SaaS database platforms (MongoDB, Neo4J, Elastic, Redis), including consolidation and cost-optimization initiatives at enterprise scale
• Demonstrated success owning DBaaS and defining fit-for-purpose database architecture patterns adopted by application/product teams
• Demonstrated experience with agentic AI and data platform concepts — MCP servers, Knowledge Catalog, Conversational Analytics, and AI governance frameworks
• Strong background in DevOps/SRE practices, automation, CI/CD, and observability
• Bachelor's degree required
• Candidates for positions with Ford Motor Company must be legally authorized to work in the United States
• Visa sponsorship is not available for this position
• Excellent executive communication skills and ability to lead through influence
• Master's degree preferred