> Markdown version of [/jobs/ext/4674948-manager-data-systems](https://www.wearedevelopers.com/jobs/ext/4674948-manager-data-systems). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Manager, Data Systems - **Company:** Netflix, Inc. - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Airflow, Data Deduplication, Information Engineering, Data Systems, Machine Learning, Data Streaming, Feature Store, Apache Spark, Core Data, Apache Flink, Apache Kafka, Data Management, Data Pipelines - **Published:** November 11, 2026 - **Apply:** https://startup.jobs/senior-manager-data-systems-aims-netflix-com-10736175 ## About the Role * 8+ years in data engineering, data platforms, or analytics engineering, including 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. * Experience owning user event and engagement data end to end: 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., * 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 ML 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. ## Description We're looking for a Senior Manager to lead the AIMS Data Systems team, which builds the web-scale datasets and systems behind personalization, recommendations, and product optimization. The team collects, processes, and attributes signals from member interactions, content, and business operations, turning them into trusted data that improves the member experience. You'll set strategic and technical direction for large-scale batch and streaming pipelines. You'll ensure that data is accurate, timely, well-documented, and trusted by product, data science, ML, and engineering partners. As a senior leader, you'll hire and develop strong engineers, set priorities across competing needs, and communicate clearly with technical and non-technical stakeholders. You'll balance near-term delivery with a long-term platform strategy, modernizing core data pipelines and schemas while keeping downstream consumers stable. What you'll do * Lead and develop a team building the ingestion, processing, and attribution systems behind engagement, content, and business data. * Set the roadmap and technical direction for batch and streaming data platforms, in partnership with product, data science, ML, 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.