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iSpot.tv is seeking an Engineering Manager to lead the Publisher Team, responsible for mission-critical systems that ingest, process, and organize massive streams of media data. This role owns the end-to-end data lifecycle from raw broadcast/digital signal ingestion through robust Content Catalog management, serving as the source-of-truth foundation for all downstream measurement and attribution products.
Key responsibilities include leading platform evolution through system modernization, upgrading and refactoring existing ingestion and cataloging systems to improve throughput and reduce latency. You will embed machine learning and AI into content cataloging workflows to automate creative identification, metadata extraction, and fingerprinting at scale. The role requires deep expertise in managing massive ad datasets and partnering cross-functionally to evolve platform capabilities for complex attribution modeling.
You will own engineering work to onboard and maintain large third-party data sources including ACR, set-top-box, streaming, and panel signals. Full lifecycle ownership spans design and development through testing, release, and post-release stability. You are the final authority on release readiness, defining and enforcing software quality standards while tracking defect density, test coverage, and production uptime.
The role balances people leadership with technical execution. You will coach and mentor a team of engineers, fostering a culture of high accountability and quality-first engineering. Responsibilities include effective sprint planning to translate long-term strategy into actionable sprints, owning both backend-heavy systems and the architecture of internal-facing tools and dashboards. You will ensure 24/7 availability and performance of the ad catalog and ingestion engines while driving agile best practices.
Required qualifications include proven experience managing high-performing teams delivering production-grade distributed software at scale, deep domain expertise in big data architectures with high-throughput data ingestion or media metadata systems, and a track record of upgrading legacy systems and refactoring technical debt. Experience embedding AI/ML models into production workflows for content cataloging and data tagging is essential, along with architectural proficiency across full-stack systems.
