Senior Manager, Content Promotion & Distribution Data Engineering

We are looking for a seasoned leader to manage our Content Promotion & Distribution Data Engineering team.

- 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 to set best practices for data storage, access, and usage by analytics engineers, data scientists, and software/ML engineers.

- Hire, lead, and develop a stunning 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:

- Traditional data engineering craft: batch/streaming pipelines, data modeling, data warehousing, data quality, and reliability for analytics and experimentation.

- Multi-modal, ML-ready data: 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. :

- Content media and ML foundations, including scalable access to media assets

- Emerging GenAI/ML use cases in promotion and creative automation (e.g., Synthetic voice, machine translation, etc.)

- 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:

- 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.

- You have been leading data engineering teams for 7+ years , including managing managers and larger, heterogeneous teams (data, software, ML engineers) or shared-resource teams.

- You have a proven track record 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.

- 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), even if you are not writing production code every day.

- You are a 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.

- You are deeply invested in creating an inclusive team environment and helping each team member grow; you care about psychological safety, diversity of perspectives, and clear, actionable feedback.

- You have experience leading a team of shared resources , effectively prioritizing and sequencing work across multiple domains and stakeholder groups.

- You are an experienced partner for ML Platform, Content Engineering, and Data Platform teams; you can advocate from a data engineering perspective and align on shared components and standards.

- You have deep technical expertise in one or more aspects of data engineering, such as:

- Media or other large-scale, multi-modal asset processing pipelines

- Media or other large-scale, multi-modal asset processing pipelines

- You're comfortable with a collection of Big Data and cloud-based tech (e.g., S3 or similar object storage, Spark or other distributed processing frameworks, modern data warehouses, workflow orchestration), and are able to make sound architecture and infrastructure tradeoffs.

You are curious, reflective, humble, and impact-oriented ; you seek feedback, learn from mistakes, and can pivot when things aren't working.

Generally, our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range.

The range for this role is $525,000.00 - $950,000.00.