VP, Product Engineering (Software)

Scientific Games is the global leader in lottery games and sports betting technology, seeking a VP, Product Engineering to lead a high-impact team focused on building new products. This role involves driving the product engineering lifecycle, partnering with stakeholders, and delivering high-quality software while maintaining platform stability.

Responsibilities:

• Lead product engineering for strategic new business opportunities, from problem framing through prototype, validation, production readiness, and scale
• Partner with product and business leaders to decide what is worth building, what should be built first, and how success will be measured
• Build and lead a small, high-performance engineering team with strong technical judgment, product judgment, ownership, and learning speed
• Establish an AI-first development model grounded in clear specs, TDD / ATDD, automated testing, fast feedback loops, CI/CD, observability, and production-safe release practices
• Move quickly inside legacy systems while improving the parts of the platform the team touches: cleaner interfaces, better tests, stronger automation, and less future drag
• Model a faster, safer product engineering motion for SG by showing how AI-assisted development, strong specs, strong tests, and low cycle time can work together at enterprise scale

Requirements:

• 12+ years of experience across software engineering, product engineering, platform engineering, or technical product development
• 5+ years leading high-performing engineering teams or technical pods
• Proven track record of building and delivering real software in fast-paced, high-ownership environments
• Experience operating effectively within large, complex systems, including legacy platforms
• Strong understanding of customer commitments, security, compliance, and production reliability requirements
• Leading end-to-end product engineering: problem framing, technical design, implementation, testing, release, production operation, and iteration
• Building new products, platforms, or capabilities from early ambiguity to production use
• Working in or around legacy systems while improving architecture, testability, operability, and delivery speed
• Using modern AI-assisted software development practices, including coding assistants, agentic workflows, AI-assisted code generation and review, test generation, documentation support, migration support, and workflow automation
• Practicing specification-driven development: clear intent, acceptance criteria, interface contracts, examples, non-functional requirements, and release criteria
• Building strong automated testing systems, including TDD, ATDD, unit testing, integration testing, contract testing, regression automation, performance testing, and production validation
• Lottery, gaming, payments, regulated transactional systems, or other high-reliability customer-facing platform experience
• Polyglot environments with multiple languages, platforms, deployment models, and legacy architectures
• Cloud-native development, platform engineering, infrastructure as code, containerized workloads, APIs, event-driven systems, and distributed systems
• Test automation across unit, integration, contract, regression, performance, and end-to-end testing