AI-Financial Platforms Lead
Location: Dallas, TX (Hybrid)

Oracle
SQL Server
Database Platforms

Do you have experience with major database platforms such as Oracle, SQL Server, PostgreSQL, MySQL, MongoDB, or cloud-managed databases?
Do you have experience with cloud platforms such as AWS, Azure?

AI-Financial Platforms Lead - Executive Summary
• 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.
Business Impact
• Reduces operational risk through intelligent automation and standardized platforms.
• Improves performance, availability, reliability, and security of enterprise databases.
• ccelerates 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.