Overview As an Engineering Manager on Netflix's Ads Measurement team, you lead a high-performing group responsible for quantifying campaign outcomes and surfacing advertiser insights at scale. You champion ownership, cross-functional collaboration, and a product-minded approach to delivering rigorous measurement and attribution solutions. You'll shepherd strategic initiatives in a fast-paced, startup-like environment within Netflix, enabling impact for advertisers while preserving the viewer experience. This role offers the chance to help shape measurement infrastructure for a growing Connected TV ads ecosystem.
Compensation / Benefits- Health Plans
- Mental Health support
- 401(k) Retirement Plan with employer match
- Stock Option Program
- Health Savings and Flexible Spending Accounts
- Family-forming benefits
Responsibilities- Drive success in a fast-paced, flat organization with minimal process and strong ownership
- Contextualize the broader vision to your team, enable prioritization, and foster executional excellence
- Hire, retain, and grow high-performing engineering talent to build capabilities in measurement
- Serve as an ambassador of the Netflix culture and ensure alignment with company values
Key requirements- 10+ years total experience with 3+ years of management in diverse software engineering teams
- Deep expertise in first-party measurement solutions (conversion lift, brand lift, geo lift, incrementality) and end-to-end experimentation frameworks
- Proven leadership of teams building causal inference and attribution systems
- Experience building scalable data pipelines and measurement APIs; understanding tradeoffs between precision, privacy, and reporting needs
- Strong product mindset with track record delivering large, complex projects via cross-functional collaboration
- General understanding of the advertising marketplace and landscape
- Strong analytical and strategic thinking with demonstrated leadership and product sense
- leadership
- communication
- strategic thinking
- measurement systems
- experimentation frameworks
- causal inference
