Details:
AI-First Data Platforms Lead – Executive Summary
Location: Dallas TX-Onsite
- Own the enterprise database platform strategy, architecture, governance, and technology roadmap.
- Lead the transformation from traditional DBA operations to an AI-first Database Platform Engineering model.
- Drive AI-powered automation for database provisioning, monitoring, maintenance, performance tuning, and incident management.
- Build and manage self-service database provisioning capabilities to accelerate engineering delivery and reduce manual effort.
- Ensure database platforms are secure, scalable, resilient, highly available, and cost-efficient across on-premises and cloud environments.
- Lead database modernization, consolidation, migration, and cloud adoption initiatives.
- Establish standards, best practices, governance, and lifecycle management for enterprise database platforms.
- Implement observability, predictive monitoring, and AIOps capabilities to proactively prevent outages and improve reliability.
- Partner with Engineering, Infrastructure, Security, Architecture, and Application teams to deliver platform services and approved patterns.
- Drive adoption of Infrastructure-as-Code (IaC), DevOps, CI/CD, and Database-as-a-Service (DBaaS) capabilities.
- Ensure compliance, data protection, access controls, backup, recovery, and disaster recovery readiness.
- Mentor and develop database engineers while fostering a culture of automation, innovation, and operational excellence.
- Evaluate emerging database, AI, and cloud technologies to continuously improve platform capabilities.
- Optimize platform costs through standardization, automation, capacity planning, and resource utilization.
Job Requirements
Details:
Business Impact
- Reduces operational risk through intelligent automation and standardized platforms.
- Improves performance, availability, reliability, and security of enterprise databases.
- Accelerates provisioning from days to minutes through self-service capabilities.
- Enhances compliance and governance while reducing manual administrative effort.
- Lowers long-term support and infrastructure costs through automation and platform rationalization.
- Enables engineering teams to move faster with AI-enabled platform services and expert guidance.
- Creates a scalable foundation that supports enterprise growth, cloud strategy, and future AI initiatives.
Key Success Measures
- Significant reduction in manual DBA effort through AI and automation.
- Faster database provisioning and deployment cycles.
- Improved uptime, reliability, and recovery capabilities.
- Reduced incident volume and Mean Time to Resolution (MTTR).
- Increased adoption of self-service database services.
- Lower total cost of ownership (TCO) through optimization and standardization