Analytics Engineer (L5) - Data Management & Analytics, Consumer Insights

Key Skills

Data Analytics, Data management, Data pipelines, Data science techniques, Data Visualization, ETL frameworks, Github, Looker, Optimization, Python Programming, R Programming, R-Shiny, SQL

Job Description

We’re looking for a passionate Analytics Engineer to join our Consumer Insights Data Management & Analytics (DM&A) team. This team leverages technical expertise in applied data science, engineering, and solutions development to support a high-impact team of 90+ quantitative and qualitative researchers. The Consumer Insights team conducts cutting-edge research designed to help answer Netflix’s most complex business challenges.

In this role, you will own and develop next-generation data pipelines, data models, tools and powerful dashboards to elevate consumer research and help answer Netflix’s most complex business challenges.

Visit our Research page to learn about what it’s like to work on Analytics at Netflix.

In this role, you will:

• Create and maintain scalable data pipelines to manage and transform large amounts of survey, social and behavioral data for analytics use cases and company-wide reporting.
• Bring vision to a developing space, identifying the root question(s) to solve for, and how to unearth insights that will inform key business decisions.
• Be a strategic partner to Consumer Insights Researchers: Helping to identify critical analytical problems and find innovative solutions with data.
• Create highly intuitive, visual dashboards with a focus on high business impact.
• Take ownership of existing dashboards and data tools with an eye for improving those resources to facilitate self-service analytics.
• Collaborate with Researchers, Engineers and Data Scientists to conduct analyses and apply statistics to answer specific day-to-day questions or support larger initiatives.
• Develop data-driven stories to answer some of the toughest questions in entertainment through data visualization and the summarizing of insights in verbal and written form.

To be successful in this role, you have:

• Advanced degree in Statistics, Computer Science, Mathematics or a related field or relevant industry industry experience.
• Expert programming and data manipulation skills using SQL, R and/or Python, and version control (Github, Stash).
• Experience building multi-step ETL jobs/data pipelines and working with job scheduling systems.
• Experience in developing dimensional warehouse data models.
• Strong data visualization skills and experience with Tableau are required. This includes viz development, complex dashboard actions, and optimizations. Familiarity with other data visualization tools is a plus (e.g. R Shiny, Looker).
• Demonstrated ability to thrive with minimal oversight and process. You connect the dots across business needs and independently drive projects forward.
• Experience working with 3rd party APIs for data ingestion is a plus.
• Experience working with or interest in survey data and/or social data is a plus.

Tags: ResearchSite

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.