Data Engineer (L6) - Ads (Signals and Measurement)

Netflix is a streaming entertainment company seeking a senior Data Engineer to lead its Ads Signals & Measurement team. The role sets technical direction across conversion and attribution, measurement data foundations, audience onboarding, and identity, while mentoring engineers and partnering on platform-wide data initiatives.

Responsibilities:

• Set technical direction and design coherence across the various orgs, connecting the dots between domains rather than optimizing just one
• Weigh in on and help resolve cross-domain design decisions, helping teams converge on approaches that scale across the org, not just within a single team
• Mentor and coach data engineers, helping them grow technically and adopt AI and emerging tools and technologies
• Build strong partnerships with senior engineers on our data platform and D&I teams, contributing to and shaping horizontal and platform-wide efforts

Requirements:

• • A seasoned technologist with 12+ years building data-foundation, backend, or data engineering systems, someone other engineers naturally look to for direction
• • You've spent real time in advertising or ad-tech data, specifically in domains like Ads identity graph, measurement, targeting, and while your depth may run deepest in one domain, you're comfortable moving across several
• • You've built trust and driven technical direction across organizational lines, not just within your own team, and you've done it in more than one company or role
• • You take data privacy and governance seriously, not as a checkbox, but as something you've actually built for
• • You're a clear, thoughtful communicator who can translate technical nuance for engineers and business context for leaders, often in the same conversation
• • You're comfortable with ambiguity, and just as comfortable helping a room of smart, opinionated engineers land on a shared answer
• • You genuinely enjoy helping other engineers grow, and see that as just as core to the job as your own technical output
• • Modern data stacks end-to-end: ingestion, processing, storage, and serving
• • Ad-tech systems and data flows, ideally with direct exposure to at least 2 domains across measurement, attribution, identity , audience, targeting
• • Large-scale distributed data systems such as Spark, Kafka, or columnar stores
• • The latest in AI and agentic technologies as they apply to data engineering
• • A graduate degree in Computer Science with a data focus is a plus, as is experience formally mentoring engineers on their technical growth