Lead Software Engineer - Ad Serving Engineering at The Walt Disney Company

Glendale, California, United States

Not SpecifiedCompensation

Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Entertainment, Media, Advertising, SportsIndustries

Deep technical knowledge and skills in delivering scalable, performant, maintainable, loosely coupled, encapsulated, and testable software
Willingness and desire to effectively communicate and collaborate across teams and systems
Demonstrated project ownership
Expertise in system and API design, and implementation best practices (e.g., micro-service encapsulation and loose coupling) to guide and mentor others
Passion for mentoring, learning, and adapting to a very dynamic and fast-paced environment
Proven ability to work with product teams to translate requirements into well-defined technical implementations
Ability to define technical and operational metrics to measure system health
Keen eye for potential optimizations and enhancements
Kindness and pragmatic optimism
Deep understanding of Java and software development

Develop and support ad delivery systems for on-demand and live TV content
Work on services that handle high-throughput ad requests, focusing on performance, reliability, and evolving the platform to support new ad formats
Build, enhance, and maintain the high-performance, distributed, microservice-based Digital Advertising Platform for qualification, delivery, and tracking of Digital Advertising campaigns (Video on Demand, Live TV, and innovative interactive ads) across Disney online properties (Hulu, Disney+, ESPN+)
Collaborate in the development of supporting components, such as anti-ad fatigue systems, impression counting pipelines, and ad experience flows
Work on integrations with critical services responsible for entitlements, correct pacing, targeting, selection of ads, and third-party systems
Apply technical expertise using stack including Java, Spring, Kubernetes, AWS, and Terraform
Guide and mentor on system and API design and implementation best practices