Meta’s products depend on data pipelines that process large-scale batch and real-time workloads. Today, those workloads span two ecosystems. Our streaming stack includes XStream and a growing managed Apache Flink footprint. Our batch stack includes Spark and Presto over Hive and Iceberg tables, Velox as a shared execution layer, and an internal workflow authoring and scheduling platform. These systems evolved separately. As a result, engineers encounter different authoring models, operational practices, guarantees, and migration paths depending on the workload they are building. We are looking for a senior individual-contributor PM to set the product direction across this portfolio. The immediate focus is to define a coherent customer experience across batch and streaming, align the long-term platform strategy, and help teams move from legacy systems safely.
Key Responsibilities
- You will work closely with engineering leaders across multiple organizations. Success will require technical judgement, a strong understanding of internal customers, and the ability to turn a cross-organizational strategy into measurable adoption and operational improvements.
Requirements
- 12+ years of experience in Product Management or equivalent relevant experience
- Experience with or direct product ownership of at least one of Google Dataflow, Apache Beam, Flink, Kafka, Spark Structured Streaming, Iceberg or comparable systems
- Demonstrated expertise in large-scale distributed data systems
- Critical thinking and analytical leadership experience
- Experience driving strategy and alignment across multiple organizations without direct authority
- BA/BS in Computer Science or Information Systems
- Fluency in streaming semantics like event time versus processing time, windowing, watermarks, state backends, exactly-once delivery, deep enough to argue the tradeoffs
- Hands on experience, for example, prior solutions architect or developer relations roles, is a strong plus
- Experience owning autoscaling, multi-tenant capacity or compute efficiency as a product outcome on a fixed or constrained fleet
- Experience with the ML side of streaming: realtime feature generation, freshness and coverage tradeoffs, training-serving consistency
- A track record of successfully deprecating a system people depended on
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
#J-18808-Ljbffr
