Netflix
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Analytics Engineer (L4) - Commerce Core
role at
Netflix
Netflix is one of the world's leading entertainment services, with over 300 million paid memberships in more than 190 countries. Members watch, pause, and resume TV series, films, and games in a wide variety of genres and languages. As Netflix's business grows, it is increasingly important to support a wide set of business stakeholders with the right data, metrics, and analytical tools. The Commerce Core team within Finance Data Science and Engineering measures Netflix’s business success, enabling partners to access key metrics, leverage data efficiently, and optimize the business globally.
In This Role, You Will
Partner with Data Engineers to design and implement robust data models and pipelines that enable richer, faster, and more accurate analysis.
Design, build, and maintain scalable, self‑service analytics tools and data products to drive adoption across the business.
Develop, define, and maintain key metrics and dashboards for executive and business‑critical reporting.
Proactively identify gaps and opportunities for innovation in analytics engineering, and drive initiatives to address them.
Experiment with and evaluate new technologies, including Generative AI solutions, to improve analytics, reporting, and data accessibility.
Translate complex data insights and technical concepts into clear, actionable recommendations for technical and non‑technical stakeholders.
We’d love to hear from you if you have
A degree in a quantitative field (e.g., mathematics, statistics, computer science, engineering, or similar) and 3+ years of experience in data engineering, data science, analytics engineering, or a related field.
Proficiency in PySpark or Scala Spark, with experience building and scaling data models, pipelines, and analytics tools.
Advanced SQL skills, with experience working in complex data environments.
Experience with scripting languages, particularly Python.
Familiarity with modern data technologies such as Trino, Spark, and Snowflake.
Understanding of data governance, data lineage, and cataloging, and their importance for data quality and automation.
Experience with data visualization platforms (e.g., Tableau, Looker, Plotly) and strong ability to communicate data‑driven stories.
Familiarity with Generative AI/ML concepts and a passion for experimenting with new technologies.
Excellent verbal and written communication skills, with the ability to articulate metric definitions, data insights, and project roadmaps to diverse audiences.
Alignment with Netflix’s culture: you work independently, thrive in ambiguity, and love continuous learning.
Experience in e‑commerce or subscription‑oriented businesses is a plus.
Our compensation structure consists solely of an annual salary; there are no bonuses. The compensation range for this role is $170,000 - $720,000. You choose annually how much of your compensation you want in salary versus stock options based on market indicators and your specific role and experience.
Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, a Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family‑forming benefits, and Life and Serious Injury Benefits. Paid leave of absence programs are available, and full‑time salaried employees are immediately entitled to flexible time off.
We are an equal‑opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully and do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender identity, or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.
If you need an accommodation or adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner.
Netflix has a unique culture and environment. Learn more at the provided culture link.
#J-18808-Ljbffr
Analytics Engineer (L4) - Commerce Core
role at
Netflix
Netflix is one of the world's leading entertainment services, with over 300 million paid memberships in more than 190 countries. Members watch, pause, and resume TV series, films, and games in a wide variety of genres and languages. As Netflix's business grows, it is increasingly important to support a wide set of business stakeholders with the right data, metrics, and analytical tools. The Commerce Core team within Finance Data Science and Engineering measures Netflix’s business success, enabling partners to access key metrics, leverage data efficiently, and optimize the business globally.
In This Role, You Will
Partner with Data Engineers to design and implement robust data models and pipelines that enable richer, faster, and more accurate analysis.
Design, build, and maintain scalable, self‑service analytics tools and data products to drive adoption across the business.
Develop, define, and maintain key metrics and dashboards for executive and business‑critical reporting.
Proactively identify gaps and opportunities for innovation in analytics engineering, and drive initiatives to address them.
Experiment with and evaluate new technologies, including Generative AI solutions, to improve analytics, reporting, and data accessibility.
Translate complex data insights and technical concepts into clear, actionable recommendations for technical and non‑technical stakeholders.
We’d love to hear from you if you have
A degree in a quantitative field (e.g., mathematics, statistics, computer science, engineering, or similar) and 3+ years of experience in data engineering, data science, analytics engineering, or a related field.
Proficiency in PySpark or Scala Spark, with experience building and scaling data models, pipelines, and analytics tools.
Advanced SQL skills, with experience working in complex data environments.
Experience with scripting languages, particularly Python.
Familiarity with modern data technologies such as Trino, Spark, and Snowflake.
Understanding of data governance, data lineage, and cataloging, and their importance for data quality and automation.
Experience with data visualization platforms (e.g., Tableau, Looker, Plotly) and strong ability to communicate data‑driven stories.
Familiarity with Generative AI/ML concepts and a passion for experimenting with new technologies.
Excellent verbal and written communication skills, with the ability to articulate metric definitions, data insights, and project roadmaps to diverse audiences.
Alignment with Netflix’s culture: you work independently, thrive in ambiguity, and love continuous learning.
Experience in e‑commerce or subscription‑oriented businesses is a plus.
Our compensation structure consists solely of an annual salary; there are no bonuses. The compensation range for this role is $170,000 - $720,000. You choose annually how much of your compensation you want in salary versus stock options based on market indicators and your specific role and experience.
Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, a Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family‑forming benefits, and Life and Serious Injury Benefits. Paid leave of absence programs are available, and full‑time salaried employees are immediately entitled to flexible time off.
We are an equal‑opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully and do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender identity, or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.
If you need an accommodation or adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner.
Netflix has a unique culture and environment. Learn more at the provided culture link.
#J-18808-Ljbffr