Manager, Data Engineering

At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next.

Content Promotion & Distribution Data Engineering team helps enable and inform how we launch, promote, and distribute Netflix content across surfaces, channels, and markets. When a Netflix member opens our app, visits a partner device, or encounters our campaigns off-service, we have a few precious moments to help them choose a story that is right for them at that moment in time. Presenting compelling evidence — artwork, trailers, synopses, notifications, and marketing assets — that authentically represent each title and resonate with the member is essential to this effort. We also work to ensure these experiences scale globally across languages, devices, and distribution channels.

Data and insights are at the heart of how we make these decisions. Our role in Data Engineering is to make this possible: to provide robust, scalable, and well-modeled tabular and multi‑modal data foundations that power analytics, experimentation, and machine learning across the Content Promotion & Distribution ecosystem.

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. :

  • Title launch management and promotional planning

  • Analytics and optimization for promotional media 

  • 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:

  • Scalable and interpretable analytical data models

  • Reliable batch and streaming pipelines

  • 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.

• You are 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), 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

  • Building ML- and experimentation-ready data products

  • Data warehousing and dimensional/semantic data modeling

  • Batch and streaming data processing

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

  • Building ML- and experimentation-ready data products

• 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.

Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more details about our Benefits here.

Netflix is a unique culture and environment. Learn more here.

Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner.

We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.