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Netflix
Netflix

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Senior Software Engineer (L5) - Analysis

職種エンジニアリング
経験シニア級
勤務地USA - Remote
勤務リモート
雇用正社員
掲載1ヶ月前
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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 Analysis team in Experimentation Platform builds and operates the core systems that power A/B test analysis across Netflix. Our tools enable confidence in business decisions that affect our members.

We are looking for a Senior Distributed Systems Engineer to design, build, and operate the next generation of analysis tooling at Netflix.

What You’ll Do

  • Build and evolve critical experiment analysis tooling

Build, maintain, and improve real-time and batch analysis workflows for experiment analysis, regression detection, and more

  • Own reliability and performance

Participate in on-call, lead incident response, and drive long-term reliability improvements

  • Instrument services with rich observability (metrics, logs, traces) and continuously tune for resilience, performance, and scalability

  • Shape data and integration surfaces

Collaborate with teams using technologies like Flink, Spark, Elasticsearch, and Druid to ensure experimentation data is correct, timely, and usable

  • Define clear data and API contracts for consumers and pipelines

  • Partner with product engineering teams

Deeply understand experimentation workflows across Netflix

  • Simplify and improve the experience for monitoring and deciding experiments

What Sets You Apart

  • Strong understanding of experimentation lifecycle and risks
    You’ve done it before: run or facilitated experiments with large business implications, understanding the risks and rewards of experimentation at scale. A background in data science/statistics is a big plus.

  • Backend & distributed systems depth
    You’ve built and operated reliable and observable backend workflows and understand how to apply core algorithms, and systems design to real-world distributed systems.

  • Strong coding & operational rigor
    You write high-quality code and systems, and have owned production services end-to-end: monitoring, on-call, debugging, and systematic performance and reliability improvements.

  • High-impact collaboration & product sense
    You work effectively with other engineers and cross-functional partners, bring clarity to ambiguity, drive decisions, and keep a sharp focus on delivering value to internal users and stakeholders.

Why This Role Is Unique

  • Central to how Netflix learns: Your work underpins how nearly every product team experiments and rolls out features.

  • High leverage & visibility: You will build foundational services used by engineers, data scientists, and product partners across the company.

  • Challenging engineering problems: Our analysis workflows process billions of events per day - and the speed of processing these events directly affects business outcomes.

  • Real ownership: You will influence architecture and roadmap while working with a senior and highly engaged team.

Generally, 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 $388,000.00 - $558,000.00. This compensation range will vary based on location.

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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Netflixについて

Netflix

Netflix

Public

An online streaming platform that enables users to watch TV shows and movies.

10,001+

従業員数

Los Gatos

本社所在地

$280B

企業価値

レビュー

10件のレビュー

3.8

10件のレビュー

ワークライフバランス

2.8

報酬

4.2

企業文化

3.9

キャリア

3.8

経営陣

3.2

68%

知人への推奨率

良い点

Great benefits and compensation

Innovative and diverse culture

Supportive team and management

改善点

Fast-paced and high pressure environment

Work-life balance issues and long hours

High workload and expectations

給与レンジ

1,877件のデータ

Mid/L4

Mid/L4 · ANALYTICS ENGINEER

7件のレポート

$274,996

年収総額

基本給

$211,536

ストック

-

ボーナス

-

$274,996

$358,605

面接レビュー

レビュー3件

難易度

3.7

/ 5

体験

ポジティブ 0%

普通 67%

ネガティブ 33%

面接プロセス

1

Application Review

2

Recruiter Screen

3

Technical Phone Screen

4

System Design Interview

5

Onsite/Virtual Interviews

6

Final Round

よくある質問

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge

Culture Fit