Manager, Engineering

Overview In this role you lead the Publisher Team to build a scalable, high-quality data platform for ingesting, processing, and cataloging massive media streams. You will own end-to-end lifecycle and ensure platform reliability, accuracy, and performance that underpin downstream measurement. You’ll balance people leadership, technical execution, and quality standards while guiding frontend tooling and ads domain knowledge. This is a hands-on, impact‑driven leadership role in a fast-growing ad-tech environment with AI-enabled enhancements. You’ll collaborate across Product, Data Science, and Operations to modernize the stack and accelerate data-driven insights. Compensation / Benefitsbase salary plus variable commission and annual bonus targetsequity plan with stock optionshybrid and flexible workplace policyfull-time benefits for eligible employeesremote-friendly options depending on locationovertime pay for non-exempt roles (pre-approved) ResponsibilitiesLead end-to-end pipeline development from design to release and post-release stabilityDefine and enforce software quality gates, including defect metrics and uptimeOwn frontend and internal tooling architecture for internal power usersDrive platform modernization to improve throughput, reduce latency, and reduce technical debtChampion AI-assisted engineering to automate cataloging and metadata taggingOversee data onboarding from third-party sources and ensure smooth migrationsPlan and execute in sprints, balancing new features withLead RCA on production incidents and drive continuous improvementEnsure 24/7 availability and performance of ad catalog and ingestion enginesFoster a culture of accountability and quality-first engineeringCollaborate with Product, Data Science, and Operations to align engineering efforts Key requirementsProven leadership of high-performing, scalable distributed software teamsDeep Big Data domain expertise with high-throughput ingestion and media metadata systemsTrack record of system modernization and refactoring to scalable architecturesExperience embedding AI/ML into production workflows for automationFull-stack architectural proficiency for backend data systems and internal toolingLifecycle ownership from design to post-release stability and performanceQuality governance with automated testing (unit/integration/regression) and CI/CDOperational excellence using data-driven metrics and RCAStrong agile leadership and sprint planning skillsStrategic collaboration with cross-functional partnersStrong communication and collaborationAccountability and ownershipProblem-solving under pressureBig data architecturesData ingestion pipelinesContent cataloging and metadata systems