Sr Data Engineer

Disney Disney · Media · San Francisco, CA +2

Senior Data Engineer to design, build, and scale data foundations for AI adoption in Ad Technology. Owns data flow into AI-ready stores, including streaming, embedding, and vector stores. Partners with AI Core Engineering to enable shared agents and developer tools with real-time data. Builds resilient pipelines for AI applications, focusing on observability, quality, and reliability.

What you'd actually do

  1. Build and maintain high‑performance streaming and batch data pipelines that power AI applications, ensuring reliable low‑latency ingestion and high‑throughput processing.
  2. Implement and extend embedding generation workflows, vector store integrations, and retrieval pipelines supporting semantic search, RAG systems, and AI assistants.
  3. Develop and optimize scalable storage and retrieval patterns, focusing on cost‑efficient architecture and smooth production performance.
  4. Implement AI‑optimized data models and storage patterns that align with broader enterprise architecture and platform requirements.
  5. Embed end‑to‑end observability into data systems, including metrics, structured logging, automated alerts, drift detection, and failure analysis.

Skills

Required

  • Python
  • Java
  • SQL
  • streaming data pipelines
  • large-scale distributed environments
  • data engineering

Nice to have

  • embedding generation workflows
  • vector store integrations
  • retrieval pipelines
  • semantic search
  • RAG systems
  • AI assistants
  • cost-efficient architecture
  • production performance
  • AI-optimized data models
  • enterprise architecture
  • platform requirements
  • feature stores
  • observability
  • structured logging
  • automated alerts
  • drift detection
  • failure analysis
  • data quality validation
  • schema evolution safeguards
  • governance/compliance controls
  • reliability
  • recoverability
  • auditability
  • long-term maintenance
  • prototyping
  • implementation
  • testing
  • deployment
  • optimization
  • documentation
  • infrastructure
  • ML engineering
  • product
  • governance teams
  • technical proposals

What the JD emphasized

  • AI adoption
  • AI applications
  • AI assistants
  • AI Core Engineering
  • AI-optimized data models
  • AI platform services

Other signals

  • AI adoption
  • data foundations
  • streaming pipelines
  • embedding pipelines
  • vector stores
  • RAG systems
  • AI assistants