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Warner Bros. Discovery is seeking a Staff Engineer to design and deliver scalable, cloud-native solutions for their advertising technology stack. The ideal candidate will have extensive experience in software engineering, specifically in AdTech and cloud environments, and will be responsible for modernizing legacy systems while mentoring junior engineers.
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Job Summary
This role is accountable for designing and delivering scalable, cloud-native solutions that power the advertising technology stack supporting billions of ad impressions monthly.
The position involves transforming legacy broadcast and scheduling workflows into scalable, cloud-native systems to enable automation and interoperability across sales and operations.
Warner Bros. Discovery offers career-defining opportunities, thoughtfully curated benefits, and a supportive culture where employees are celebrated and encouraged to thrive.
Matching Summary
Match Score: 75
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Warner Bros. Discovery is seeking a Staff Engineer to design and deliver scalable, cloud-native solutions for their advertising technology stack. The ideal candidate will have extensive experience in software engineering, specifically in AdTech and cloud environments, and will be responsible for modernizing legacy systems while mentoring junior engineers.
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Salary
Base: $146,440.00 - $271,960.00 per year; Bonus/Equity: Annual bonuses, short- and long-term incentives available; Benefits: Health insurance, wellness program, life/disability insurance, retirement plan, paid time off
Skills & Requirements
Must-have
8+ years software engineering experience
Java Scala or Python expertise
Cloud-native microservices architecture
AdTech domain knowledge
Linear broadcast systems experience
Big data frameworks Spark Kafka
Nice-to-have
AI-driven automation system design
Mentoring junior engineers
Collaboration with product teams
API-first design principles
Cost optimization strategies
Key Requirements
Bachelor's or Master's degree in Computer Science
8+ years in software engineering
Experience with AdTech or large-scale distributed systems