Manager, Data Engineering

Netflix•Los Angeles, CA

11d•$446,000 - $752,000•Remote

About The Position

The Content Promotion & Distribution Data Engineering team at Netflix is responsible for enabling and informing how the company launches, promotes, and distributes content across various platforms, channels, and markets. This involves presenting compelling data, artwork, trailers, and other assets to help members discover content. The team ensures these experiences are globally scalable across languages, devices, and distribution channels. Data and insights are central to decision-making, and the Data Engineering role is to provide robust, scalable, and well-modeled data foundations for analytics, experimentation, and machine learning within the Content Promotion & Distribution ecosystem. The team owns core analytical data models and pipelines, builds and operates multi-modal data foundations for ML and GenAI, and partners with cross-functional teams to establish best practices for data storage, access, and usage.

Requirements

7+ years of experience leading data engineering teams, including managing managers and heterogeneous teams.
Proven track record leading innovative 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 (ETL/ELT, modeling, warehousing, data quality) and ML-focused data engineering (feature pipelines, media and multi-modal data preparation, training/serving data sets).
Crisp communicator who develops strong relationships with technical and non-technical stakeholders and can drive alignment across Director/Manager-level partners.
Deeply invested in creating an inclusive team environment and helping team members grow, with a focus on psychological safety, diversity of perspectives, and clear feedback.
Experienced partner for ML Platform, Content Engineering, and Data Platform teams.
Deep technical expertise in one or more aspects of data engineering, such as media or other large-scale, multi-modal asset processing pipelines, building ML- and experimentation-ready data products, data warehousing and dimensional/semantic data modeling, or batch and streaming data processing.
Comfortable with Big Data and cloud-based technologies (e.g., S3, Spark, modern data warehouses, workflow orchestration) and able to make sound architecture and infrastructure tradeoffs.
Curious, reflective, humble, and impact-oriented.

Nice To Haves

Experience managing managers and larger, heterogeneous teams (data, software, ML engineers) or shared-resource teams.

Responsibilities

Hire, lead, and develop a team of Data and ML Engineers with diverse skill sets.
Own the end-to-end data foundations for Content Promotion & Distribution, including traditional data engineering (pipelines, modeling, quality, reliability) and multi-modal, ML-ready data (media, text, metadata for ML/GenAI).
Partner with cross-functional leaders to ideate, prioritize, and execute on high-impact data products and tools.
Steer impactful work on foundational data and media products supporting Content Promotion & Distribution, including agentic solutions, multimodal media understanding, and generation.
Provide technical vision and strategy for data modeling, storage, transformation, and serving of structured and multi-modal data.
Balance near-term stakeholder needs with long-term investments in infrastructure and ML/GenAI research and productionization.
Set and raise the technical bar for data engineering craft, focusing on scalable models, reliable pipelines, and well-governed datasets.
Drive alignment in ambiguous situations by clarifying trade-offs and making principled decisions.
Grow and mentor the team through coaching and feedback, supporting career development.
Build relationships with a wide network of stakeholders to enable decisions affecting members and content investments.
Lead a team of shared resources, effectively prioritizing work across multiple domains and stakeholder groups.
Partner with ML Platform, Content Engineering, and Data Platform teams, advocating for data engineering perspectives and aligning on shared components.