Jobs at Scientific Games

The PostgreSQL Database Administrator will manage large-scale databases, perform operational activities, implement best practices, and ensure security and performance. They will support production databases and collaborate with teams for documentation and reporting.

Support and operate customer-facing iLottery and loyalty platforms with focus on Kubernetes (on-prem and cloud), GitOps-driven CI/CD (ArgoCD), container management, observability (Graylog, New Relic, ELK, Prometheus/Grafana), infrastructure automation (Ansible, Terraform), incident response, SLOs, capacity/resiliency planning, security remediation, documentation, and 24x7 on-call support.

Provide tier-2 technical support and administration for lottery platforms, troubleshoot availability and performance issues, support customer implementations, maintain monitoring and documentation, manage SLAs, and participate in 24x7 on-call rotation. Work primarily with Windows Server and SQL Server, plus some Ubuntu and Java/WildFly support.

The Managing Director will oversee P&L accountability, drive growth strategies, manage client relationships, and lead sales cycles for Scientific Games' lottery and sports betting products.

The Managing Director drives strategic success across the lottery jurisdiction with full P&L accountability, develops account plans, manages client relationships, and ensures project delivery while maximizing profitability and growth.

Senior Product Engineer will build new products and prototypes, improve legacy systems, use AI-assisted tools to accelerate development, define specs and tests, enforce strong automated testing and CI/CD, partner across product and ops, and drive production-ready, observable, and maintainable releases.

Provide system administration and operational support for lottery platforms: implement new customers, troubleshoot availability/performance, monitor capacity and security, manage incidents and SLAs, maintain monitoring/KB articles, and participate in a 24x7 on-call rotation. Work on product releases, enhancements, and upgrades across Linux, databases, containers, and cloud environments.

Lead a small team to accelerate software engineering productivity across a global organization by improving developer workflows, tooling, testing, code review, documentation, and AI-enabled development practices. Identify bottlenecks, drive scalable engineering systems and reusable patterns, and measure productivity gains across polyglot teams and legacy platforms.

Lead a small engineering team to turn ambiguous opportunities into production software quickly and safely. Establish AI-first development, specification-driven practices, strong automated testing, CI/CD, observability, and production ownership while improving legacy systems and influencing enterprise engineering practices.