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

About this role

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...