Distributed Systems Engineer 6 - Ad Eventing

Netflix Netflix · Big Tech · Los Gatos, CA +2 · Engineering

Netflix is seeking a Distributed Systems Engineer 6 to lead the Ad Eventing team, focusing on building and scaling a high-throughput, low-latency pipeline for ad impression, click, and completion events. This role involves owning technical direction, architecting the end-to-end pipeline, ensuring data accuracy and reliability for billing, reporting, and ML, and driving operational excellence. The position requires significant experience in distributed systems, stream processing, and ad domain knowledge, with a blend of building and influencing.

What you'd actually do

  1. Own the technical direction of the Ad Eventing team: architecture reviews, incident leadership, capacity planning, and scaling
  2. Architect and evolve the end-to-end ad event pipeline — from client beacon receipt through validation, deduplication, enrichment, and downstream fan-out — under strict latency and throughput constraints
  3. Scale our event ingestion infrastructure to handle billions of events per day with high availability, exactly-once semantics, and sub-second processing SLAs
  4. Design and implement robust deduplication and validation systems that ensure event accuracy across retries, network failures, and client-side inconsistencies
  5. Build and operate stream processing pipelines (e.g., Kafka, Flink, or equivalent) that power real-time signals for pacing, frequency capping, and reporting

Skills

Required

  • 10+ years building distributed systems and backend services at large scale
  • 3+ years in the ads domain
  • high-throughput event ingestion and stream processing at scale — Kafka, Flink, Spark Streaming, or equivalent
  • Built and operated event deduplication, validation, and attribution systems in an ads or similarly high-stakes data environment
  • Strong understanding of exactly-once and at-least-once delivery semantics, idempotency patterns, and the tradeoffs between them
  • Experience designing event schemas and data contracts that serve multiple downstream consumers with different latency and consistency requirements
  • Familiarity with ad measurement concepts: impression tracking, viewability, click attribution, completion rates, and IAB standards
  • Track record of technical leadership across multiple teams, setting architectural direction and influencing cross-functional roadmaps
  • Comfortable at the intersection of engineering, data, and product — translating advertiser reporting requirements and billing accuracy needs into production systems
  • Demonstrated ability to operate in an environment that is a mix of big-tech scale and startup speed, delivering production-ready results on tight timelines

Nice to have

  • Experience with CTV-specific event challenges: server-side ad insertion (SSAI), VAST/VMAP beacon tracking, live event traffic spikes
  • Familiarity with third-party measurement and verification integrations (MOAT, IAS, DoubleVerify, Nielsen)
  • Built or improved billing-grade event pipelines where accuracy directly impacts revenue reconciliation
  • Experience with experimentation infrastructure for event pipelines: validating schema changes, pipeline migrations, and deduplication logic without impacting downstream consumers
  • Strong background in resiliency and reliability: ensuring pipeline availability under extreme load (live events, traffic spikes, device-side retry storms)
  • Familiarity with privacy and compliance constraints on event data (GDPR, CCPA, data minimization)
  • Experience building observability and alerting frameworks for event pipelines — detecting drops, duplicates, and latency regressions in real time.

What the JD emphasized

  • 3+ years in the ads domain
  • high-throughput event ingestion and stream processing
  • event deduplication, validation, and attribution systems
  • exactly-once and at-least-once delivery semantics
  • event schemas and data contracts
  • ad measurement concepts
  • technical leadership across multiple teams
  • engineering, data, and product
  • big-tech scale and startup speed