Staff Data Engineer (L6) - Ads

Netflix is a global entertainment company whose Ads Data Engineering team builds data products that support advertising inventory, forecasting, targeting, ad serving, pacing, and measurement. The Staff Data Engineer will set technical direction across the Ads Data Engineering organization, guide cross-domain architecture, partner with engineering and business leaders, mentor engineers, and shape AI-native data engineering practices.

Responsibilities:

• Set technical direction and design coherence across the entire Ads Data Engineering org, 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
• Partner closely with the Ads DE director and DE managers as a senior technical voice, bridging technical strategy with broader business priorities
• Mentor and coach data engineers across the org, 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
• Help define what AI-native data engineering looks like for Ads DE as the function continues to evolve
• You will be the primary technical voice in partnerships with Ads Platform Engineering, Ads Data Science, and Product; Your impact will be at the Ads level, not at the sub-domain or pipeline-level

Requirements:

• • A seasoned technologist with 15+ 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, 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've lived through the messy middle of a major platform migration or data products' journey from 0 to 1 and 1 to 100, and you have the scars and the instincts to show for it
• • You approach and design data as a product for the business with focus on users, quality, and measurable impact; You build data foundations as flywheels to unlock impact across the business, well beyond just orchestrating pipelines and serving analytics workflows
• • 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 domains like bidding, measurement, or attribution
• • 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