Distributed Systems Engineer (L5) - Commerce Insights and Data Products Engineering

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 Commerce Insights and Data Products Engineering team is responsible for data critical to optimizing our product experiences across various product canvases for both current and future Netflix members.  Our work empowers product managers and business leaders to rapidly experiment and innovate on product experiences that drive towards our goal to “ Entertain the world ”. In this role, you will partner closely with data scientists and other engineers to build low-latency data products capable of powering algorithms and machine learning models that run across our commerce, identity flows, and more. This role is ideal for an individual with an analytically bent mind coupled with a robust engineering background who can develop highly available and reliable distributed data systems and services to ensure the timely delivery of high-quality data for use in the Netflix product. About you: You are proficient in at least one major language on the JVM stack (e.g., Java, Scala) and SQL (any variant). You strive to write elegant and maintainable code, and you're comfortable with picking up new technologies. You have a product mindset and are curious to understand the business's needs. You have a naturally collaborative style to work with product management, data science, engineering, etc in service of these needs. You possess strong data intuition and know how to apply your analytical skills and data engineering fundamentals to support building high-quality data products. You have experience building applications that use large-scale distributed systems, data processing frameworks (batch and real-time) e.g. Spark, Flink, etc., and storage solutions. You understand how ML systems consume data —features, inference inputs, labels, and reward signals—and you use that knowledge to design low-latency, reliable data products that directly support personalized experiences You are passionate about making data available for self-service and wider integration. You can craft scalable systems and solutions to realize a range of product and engineering goals. You have a strong operational awareness and design multi-tenant systems that can handle high-scale and high-throughput demands while being easy to operate, monitor, scale, and maintain 24x7. You prioritize observability in your designs, ensuring systems are equipped with comprehensive monitoring, logging, and alerting to facilitate proactive issue detection and resolution. You own what you build, beyond just your code and have a passion for quality. You are comfortable working in the most agile of environments with vague requirements. You are nimble and can pivot easily when needed. You are unafraid to take smart risks. You relate to and embody many aspects of Netflix's Culture . You love working independently while also collaborating and giving/receiving candid feedback. 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 - $619,000.00. 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 ... (truncated, view full listing at source)