Senior Manager, Data Systems, AIMS

Netflix•Remote, OR

1d•$525,000 - $950,000

About The Position

Netflix is seeking a Senior Manager to lead the AIMS Data Systems team, which builds web-scale datasets and systems supporting personalization, recommendations, and product optimization. The team collects, processes, and attributes signals from member interactions, content, and business operations to produce trusted data that improves the member experience. The Senior Manager will set strategic and technical direction for large-scale batch and streaming pipelines, ensuring data is accurate, timely, well-documented, and trusted by product, data science, machine learning, and engineering partners. This leader will hire and develop engineers, set priorities across competing needs, and communicate with technical and non-technical stakeholders. The role balances near-term delivery with long-term platform strategy, modernizing core data pipelines and schemas while keeping downstream consumers stable.

Requirements

At least 8 years of experience in data engineering, data platforms, or analytics engineering, including at least 3 years managing engineers or technical teams.
Hands-on experience with large-scale batch and streaming pipelines, such as Spark, Flink, Kafka, Iceberg, and Airflow or similar orchestration tools.
Experience owning user event and engagement data end to end, including instrumentation, schema design and evolution, ingestion, and data quality.
Working knowledge of attribution concepts, such as joining exposures and outcomes, sessionization, and deduplication.
Track record of setting technical direction, prioritizing across competing stakeholders, and delivering multi-quarter roadmaps.
Strong communication skills with product, data science, and engineering partners.

Nice To Haves

Experience building data systems that feed behavioral signals back into personalization, recommendation, or machine learning models.
Deep attribution experience, including multi-touch or incrementality-aware approaches and the tradeoffs between rules-based and model-based methods.
Experience with data contracts, lineage, observability, and SLAs for business-critical datasets.
Familiarity with machine learning data needs, such as feature stores, label generation, training-data freshness, and feedback-loop risks.
Experience building or scaling a platform used by many internal teams, including self-service tooling.
Background in streaming, media, or consumer subscription products at global scale.
Comfort with privacy and governance constraints on user-level data, such as consent, retention, and regional rules.
Experience hiring, developing, and retaining senior and staff engineers, and managing through ambiguity.

Responsibilities

Lead and develop a team building ingestion, processing, and attribution systems for engagement, content, and business data.
Set the roadmap and technical direction for batch and streaming data platforms in partnership with product, data science, machine learning, and engineering teams.
Deliver reliable, timely, and well-documented data for model training, evaluation, and decision systems.
Establish standards for data quality, observability, lineage, and operational reliability across critical datasets.
Partner with measurement and experimentation teams to improve attribution methods and validate them against causal evidence.
Modernize core pipelines and schemas while maintaining stable contracts for downstream users.
Build self-service tools and clear data contracts that make trusted data easier to discover and use.