Manager, Engineering

iSpot•Bellevue, WA

18d•$168,529 - $212,710•Hybrid

About The Position

We are seeking an Engineering Manager to lead the development of our high-scale Publisher Team. This role is responsible for the mission-critical systems that ingest, process, and organize massive streams of media data. You will own the end-to-end data lifecycle, from ingestion of raw broadcast/digital signals to the management of a robust Content Catalog. This role has explicit accountability for software quality, owns the frontend, and brings ads domain expertise, including ownership of the ad catalog. Your objective is to ensure that our platform provides a highly accurate, performant, and "source-of-truth" foundation for all downstream measurement and attribution products. The role requires balancing people leadership, technical execution, and quality outcomes across all platform systems. Platform Evolution & AI Integration System Modernization: Lead the strategy for upgrading and refactoring existing ingestion and cataloging systems to improve throughput, reduce latency, and lower technical debt. AI-Driven Automation: Embed machine learning and AI into the content cataloging workflow to automate creative identification, metadata extraction, and fingerprinting at scale. Big Data Domain Expertise: Ensure the accuracy and reliability of massive ad datasets, partnering cross-functionally to evolve platform capabilities in support of complex attribution modeling. Partner Data Integration: Own the engineering work to onboard and maintain large third-party data sources (ACR, set-top-box, streaming, and panel signals), including the migration and cutover work that comes with changing providers. End-to-End Lifecycle & Quality Accountability Full Lifecycle Ownership: Own the pipeline from design and development through testing, release, and post-release stability. Quality Gating: Define and enforce software quality standards. You are the final authority on release readiness, tracking metrics such as defect density, test coverage, and production uptime. Rigorous Testing: Ensure unit, integration, and regression testing are natively embedded in the CI/CD pipeline. Lead root-cause analysis (RCA) for any production incidents to drive continuous improvement. Technical Execution & Agile Leadership Effective Sprint Planning: Translate long-term platform strategy into actionable, well-scoped sprints. Balance the delivery of new features with the maintenance of "always-on" ingestion systems. Frontend & Internal Tooling: While the focus is backend-heavy, you will own the architecture and usability of internal-facing tools and dashboards, ensuring consistency and performance for internal power users. People Leadership: Coach and mentor a team of engineers, fostering a culture of high accountability, ownership, and "quality-first" engineering. AI-Enabled Engineering: Champion the adoption of AI-assisted development practices across the team, holding the same standards for quality and review that apply to all code we ship. Operational Reliability System Health: Ensure the 24/7 availability and performance of the ad catalog and ingestion engines. Agile Maturity: Drive agile best practices to ensure the team remains nimble while maintaining the discipline required for big data environments.

Requirements

Proven experience managing high-performing teams delivering production-grade, distributed software at scale.
Deep domain expertise in big data architectures, specifically managing high-throughput data ingestion, or media metadata systems.
Proven track record of upgrading legacy systems and refactoring technical debt into scalable, modern platform architectures.
Experience embedding AI/ML models into production workflows to automate content cataloging, creative identification, or data tagging.
Architectural proficiency in both backend data systems and frontend technologies used to build performant internal management tools.
Demonstrated accountability for the end-to-end software lifecycle, from initial design through to post-release stability and performance.
Experience defining and enforcing rigorous quality gates, including automated unit, integration, and regression testing frameworks.
Strong focus on production stability, using root-cause analysis and data-driven metrics (e.g., defect rates, latency) to drive improvement.
Mastery of effective sprint planning to balance rapid delivery of new platform features with long-term infrastructure stability.
Ability to maintain a high shipping cadence while upholding strict standards for system reliability and data integrity.
Exceptional communication skills to align engineering efforts with Product, Data Science, and Operations objectives.

Nice To Haves

Ad Tech Platform experience
Ad tech, media measurement, or audience measurement platform experience
Familiarity with publisher-side or sell-side measurement — audience delivery, reach and frequency, cross-platform de-duplication
Exposure to ACR, set-top-box, panel, or streaming census data sources
Experience managing engineering work tied to enterprise partner SLAs and contractual delivery dates

Responsibilities

Lead the strategy for upgrading and refactoring existing ingestion and cataloging systems to improve throughput, reduce latency, and lower technical debt.
Embed machine learning and AI into the content cataloging workflow to automate creative identification, metadata extraction, and fingerprinting at scale.
Ensure the accuracy and reliability of massive ad datasets, partnering cross-functionally to evolve platform capabilities in support of complex attribution modeling.
Own the engineering work to onboard and maintain large third-party data sources (ACR, set-top-box, streaming, and panel signals), including the migration and cutover work that comes with changing providers.
Own the pipeline from design and development through testing, release, and post-release stability.
Define and enforce software quality standards. You are the final authority on release readiness, tracking metrics such as defect density, test coverage, and production uptime.
Ensure unit, integration, and regression testing are natively embedded in the CI/CD pipeline. Lead root-cause analysis (RCA) for any production incidents to drive continuous improvement.
Translate long-term platform strategy into actionable, well-scoped sprints. Balance the delivery of new features with the maintenance of "always-on" ingestion systems.
Own the architecture and usability of internal-facing tools and dashboards, ensuring consistency and performance for internal power users.
Coach and mentor a team of engineers, fostering a culture of high accountability, ownership, and "quality-first" engineering.
Champion the adoption of AI-assisted development practices across the team, holding the same standards for quality and review that apply to all code we ship.
Ensure the 24/7 availability and performance of the ad catalog and ingestion engines.
Drive agile best practices to ensure the team remains nimble while maintaining the discipline required for big data environments.