Senior Manager, Content Promotion & Distribution Data Engineering

Overview

Content Promotion & Distribution Data Engineering team helps enable and inform how we launch, promote, and distribute Netflix content across surfaces, channels, and markets.

• Owns core analytical data models and pipelines that power reporting, decision‑support, and experimentation.
• Builds and operates multi‑modal data foundations (e.g., text, metadata, image, video, and audio) for ML and GenAI model development and evaluation.
• Partners closely with Content Promotion and Distribution DSE, AI and Data Platform, Content Engineering to build and steward complex data and media pipelines, and set best practices for data storage, access, and usage by analytics engineers, data scientists, and software/ML engineers.

• Hire, lead, and develop a team of Data and ML Engineers across a heterogeneous skill set (data, software, and ML engineering).
• Own the end‑to‑end data foundations for Content Promotion & Distribution, spanning batch/streaming pipelines, data modeling, data warehousing, data quality, and reliability for analytics and experimentation.
• Build and operate media, text, and rich metadata pipelines that prepare data for training and serving ML and GenAI models.
• Partner with cross‑functional leaders across Content Promotion & Distribution DSE, AI ML Platform, Content Engineering, Studio Algo, and Marketing to ideate, prioritize, and execute on high‑impact data products and tools.
• Steer deeply impactful work on foundational data and media products that support Netflix’s Content Promotion & Distribution, spanning agentic solutions, multimodal media understanding, and generation.
• Provide technical vision and strategy for how we model, store, transform, and serve both structured and multi‑modal data to power analytics, experimentation, and ML at scale.
• Balance near‑term and long‑term needs, from ongoing support of stakeholder quarterly goals to multi‑year investments in infrastructure and “paved paths” for ML/GenAI research and productionization.
• Set and raise the technical bar for data engineering craft in this space, including scalable and interpretable analytical data models, reliable batch and streaming pipelines, and well‑governed, discoverable, and reusable ML feature and media datasets.
• Drive alignment in ambiguity by clarifying trade‑offs, making principled decisions, and bringing diverse partners along a shared roadmap.
• Grow and mentor the team through thoughtful observation, coaching, and courageous, honest feedback; help engineers navigate career development across data, software, and ML engineering paths.
• Build both software and social glue across a wide network of stakeholders—VPs, Directors, Managers, and ICs—enabling decisions that affect hundreds of millions of members and major content and marketing investments.

• 7+ years of leading data engineering teams, including managing managers and larger, heterogeneous teams (data, software, ML engineers) or shared‑resource teams.
• Proven track record of leading innovative, influential data engineering work in complex business domains, ideally involving multimodal media data marketing/promotion, content/media, experimentation, and/or ML/AI‑driven products.
• Comfortable owning the technical quality of both analytics‑focused data engineering and ML‑focused data engineering, even if not writing production code every day.
• Crisp communicator who develops strong relationships with a wide variety of stakeholders—technical and non‑technical—and can drive alignment across director/manager‑level partners.
• Deeply invested in creating an inclusive team environment and helping each team member grow; care about psychological safety, diversity of perspectives, and clear, actionable feedback.
• Experience leading a team of shared resources, effectively prioritizing and sequencing work across multiple domains and stakeholder groups.
• Experienced partner for ML Platform, Content Engineering, and Data Platform teams; can advocate from a data engineering perspective and align on shared components and standards.
• Deep technical expertise in one or more aspects of data engineering, such as media or other large‑scale, multi‑modal asset processing…