Job Information
Netflix Analytics Engineer (L5) - Globalization in Los Angeles, California
At Netflix, our mission is to entertain the world by connecting our members to an extensive library of stories from all over the globe. We are revolutionizing international storytelling as we deliver billions of hours of movies and TV shows per month to 200+ million members across 190+ countries in 30+ languages.
The Globalization Data Science and Engineering team is at the forefront of removing language barriers and providing a stellar member experience to all our members regardless of their language preferences. We are responsible for the translation and cultural adaptation of all aspects of member interaction, including beautiful localized user interfaces, subtitles, and dubbing of award-winning Netflix originals.
Los Angeles is a significant center for our broader Studio and Creative Production teams. We are looking for an experienced Analytics Engineer based in our LA office. In this role, you will design and develop analytic tools and systems to create more member value via improving localization quality and efficiency. You will partner with a talented cross-functional team of engineers, scientists, product managers and domain experts to shape localization strategy and deliver business impact.
Responsibilities:
Act as a strategic partner for stakeholders and cross-functional collaborators to identify business opportunities and enhance business strategies with automated data solutions.
Drive the direction and execution of your work, which spans from developing scrappy analytic tools to designing scalable analytic systems.
Partner closely with other engineers and scientists to improve foundational data models and accelerate productization of data insights.
Share your innovation and collaborate with the broader community to strengthen analytics enablement.
Become a LA ambassador for Globalization Data Science and Engineering, helping connect and partner closely with LA collaborators.
About you:
Proven track record of designing and developing scalable analytic tools and systems.
High proficiency in standard tech stack (e.g., Python, SQL, Spark) and common data visualization tools (e.g., Streamlit, Tableau).
Familiar with fundamentals of Machine Learning and Statistical Inference as well as data engineering best practices.
5+ years of relevant experience with building data products powered by big data.
Exceptional communication and collaboration skills coupled with strong business acumen.
Comfortable with ambiguity; able to take ownership, and thrive with minimal oversight and process.
Netflix culture resonates with you.
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 $170,000 - $720,000.
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 detail about our Benefits here (https://jobs.netflix.com/work-life-philosophy) .
Netflix is a unique culture and environment. Learn more here (https://jobs.netflix.com/culture) .
We are an equal-opportunity employer and celebrate diversity, recognizing that diversity of thought and background 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.
