Job description

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. About the Team Ads Data Engineering is at the core of building a data ecosystem that powers Netflix's understanding and decision-making about the impact of ads on our business. This team builds rich, connected, and easily accessible data products spanning ad inventory, forecasting, targeting, ad serving, pacing, and more. This role will lead the Ads Signals & Measurement team across three core areas: • Conversion & Attribution: Building the data and attribution capabilities that connect advertising exposure to outcomes, powering conversion measurement and ML-based optimization. • Measurement Data Foundations: Building the data foundations that power our first-party measurement solutions across the full funnel, including incrementality, lift, experimentation, and third-party measurement studies. • Audience Onboarding & Identity: Enabling audience onboarding, identity resolution, and rich signal generation to power audience and targeting solutions. Horizontally, the team connects these areas through a data flywheel where signals, identity, measurement, attribution, and optimization continuously reinforce one another. This role sits at the intersection of these domains, ensuring they come together as a cohesive data ecosystem that powers both measurement and performance optimization. We're looking for a Staff Data Engineer to help set the technical bar and lead teams across these domains. Who are you? • 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. What will you do? • 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. What (ideally) do you know? • 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. 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 $510,000.00 - $820,000.00. 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.

Requirements

Must-haves

About the Team
Conversion & Attribution: Building the data and attribution capabilities that connect advertising exposure to outcomes, powering conversion measurement and ML-based optimization.
Measurement Data Foundations: Building the data foundations that power our first-party measurement solutions across the full funnel, including incrementality, lift, experimentation, and third-party measurement studies.
Audience Onboarding & Identity: Enabling audience onboarding, identity resolution, and rich signal generation to power audience and targeting solutions.
Who are you?
A seasoned technologist with 12+ years building data-foundation, backend, or data engineering systems, someone other engineers naturally look to for direction.
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.
What will you do?
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.
What (ideally) do you know?
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.
Netflix is a unique culture and environment. Learn more here .
Job is open for no less than 7 days and will be removed when the position is filled.

Nice-to-haves

A graduate degree in Computer Science with a data focus is a plus, as is experience formally mentoring engineers on their technical growth.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlai

Platform: eightfold
Effort: medium

Benefits

Benefits detected in the listing.

401(K)

Paid Time Off