At Disney Entertainment & ESPN Technology, we're reimagining ways to create magical viewing experiences for the world's most beloved stories while transforming Disney's media business for the future. The Content Experience & Delivery team builds systems that provide data at scale to teams across Disney+, Hulu, and Star+.
As a Lead Software Engineer, you will:
- Lead the development of well-architected solutions and delivery of maintainable code
- Design and implement complex new features
- Break down work at the epic level and set project milestones
- Write code to implement new features or optimize existing services
- Participate in 24/7 on-call rotation for tier-1 critical services
- Coach and mentor teammates
- Collaborate with Data Science/ML and Product teams
Key Requirements:
- 7+ years experience crafting and operating backend services
- Bachelor's degree in Computer Science or related field
- Experience with JVM services and asynchronous programming
- Knowledge of object-oriented and functional programming patterns
- Experience with public cloud providers (AWS, Azure, GCloud)
- Strong system design and architecture skills
- Experience with observability tools (Datadog, Splunk, Grafana)
- Knowledge of messaging technologies (Kafka, Kinesis, SQS)
Benefits:
- Competitive salary range: $152,200 - $213,900 depending on location
- Medical benefits
- 401k plan
- Equity compensation
- Opportunity to work on large-scale entertainment platforms
- Collaborative and innovative work environment
Why Disney Entertainment & ESPN Technology:
- Build the future of Disney's media business
- Reach millions of users through Disney+, Hulu, ABC News, and ESPN
- Drive innovation in streaming and digital entertainment
- Work with world-class teams and technology
- Shape how audiences experience sports, entertainment & news
The role offers a unique opportunity to impact how millions of users worldwide experience Disney's content through our streaming platforms. You'll be part of a dynamic team pushing the boundaries of content delivery and recommendation systems at scale.