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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.
The Ads Data Science and Engineering team at Netflix’s mission is to help build the foundation of the ads business at Netflix. We conduct analyses and develop analytic tools, build predictive models and algorithms using machine learning, all with the goal of creating more choices and joy for our members. You’ll work closely with partner teams to build workflows, provide recommendations, and drive success on end-to-end analytics initiatives in this 0 -1 space.
We are seeking a Senior Analytics Engineer to lead the development and optimization of programmatic signals analytics for Netflix’s global Ads business. In this highly impactful role, you will build the foundational metrics, dashboards, and analysis frameworks that drive how we measure, understand, and enhance DSP adoption, audience targeting, and ROI across our programmatic ecosystem. You will collaborate closely with product, engineering, finance, and ad operations teams to inform business-critical decisions around signal strategy, inventory quality, and fraud prevention. As a subject matter expert, you will play a pivotal role in shaping the analytics vision for programmatic advertising—empowering Netflix to optimize ad delivery, maximize revenue, and ensure transparency and trust with our partners and members worldwide. Your insights and technical leadership will directly influence the future of data-driven advertising at Netflix.
In this role, you will:
Drive the analytics strategy for programmatic signals across Netflix Ads, designing and implementing frameworks to measure, optimize, and report on DSP adoption, audience targeting effectiveness, and the ROI of bidstream signals exchanged with demand-side platforms.
Build scalable, adaptable dashboards and data models to surface insights on DSP performance, targeting accuracy, informing both product optimization and monetization strategies for programmatic advertising.
Partner deeply with cross-functional teams—including Product, Engineering, Finance, Data Science, and Ad Operations—to align on business requirements, technical execution, and measurement standards for DSP integrations, signal taxonomy, and fraud prevention.
Act as a subject matter expert on programmatic signals and DSP analytics, pioneering scalable approaches to targeting measurement, inventory quality assessment, and fraud detection—driving innovation, transparency, and long-term business value for Netflix Ads.
We are looking for:
Extensive experience in programmatic adtech analytics, with a strong understanding of programmatic advertising and large-scale streaming ecosystems.
Communication superpower — the ability to communicate, influence, and connect the dots between insights and actions across a variety of stakeholders.
Fluency in SQL, workflow orchestration tools like Apache Airflow, and at least one analytics and scripting language like Python.
Mentors, brainstorms with, and enables others, especially within your functional area.
Actively contributes and fosters technical communities internally (e.g., horizontal forums, seminars, and summits) and externally (e.g., conference participation).
Exemplary stewardship of Netflix’s culture and values, particularly in terms of selflessness and ensuring open debate of ideas and encouraging inclusion of a variety of perspectives regardless of seniority.
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.
Job is open for no less than 7 days and will be removed when the position is filled.
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About Netflix

Netflix
PublicAn online streaming platform that enables users to watch TV shows and movies.
10,001+
Employees
Los Gatos
Headquarters
$280B
Valuation
Reviews
4.2
15 reviews
Work Life Balance
4.2
Compensation
4.5
Culture
3.2
Career
3.8
Management
3.0
65%
Recommend to a Friend
Pros
Very high compensation packages (430k-700k)
Fully remote work opportunities
All cash compensation structure
Cons
Lower compensation than expected in some cases
Difficult interview process
Simple/uninteresting technical problems
Salary Ranges
1,869 data points
Mid/L4
Mid/L4 · Analytics Engineer
7 reports
$274,996
total / year
Base
$211,536
Stock
-
Bonus
-
$274,996
$358,605
Interview Experience
4 interviews
Difficulty
4.0
/ 5
Offer Rate
25%
Experience
Positive 25%
Neutral 25%
Negative 50%
Interview Process
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
System Design Interview
5
Behavioral Interview
6
Team Matching
7
Final Round
Common Questions
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
Culture Fit
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