Hybrid Lead Data Engineer

at The Walt Disney Company

• Job title: Lead Data Engineer
• Job type: Full Time
• Experience level: Senior
• Salary: $152,200 - $204,100 per year
• Degree requirement: Bachelor's Degree
• Tech skills: Airflow, Java, Kafka, Python, Spark, SQL
• Location requirements: Hybrid, Glendale, United States

About the role

• Lead Data Engineer responsible for designing and building AI data foundations in Ad Tech at Disney. Collaborate with cross-functional teams to enhance data architecture and pipeline reliability.

Responsibilities

• Design and build streaming and batch pipelines for AI applications, ensuring low-latency and high-throughput data flows.
• Implement embedding pipelines and vector store integrations to support retrieval, semantic search, and AI assistants.
• Optimize storage and retrieval patterns for scalability, compliance, and cost efficiency.
• Own the design of AI-optimized data stores that align with enterprise data architecture.
• Partner with the AI Core Engineering team to provide reliable pipelines into shared agents, registries, and services.
• Develop reusable data ingestion and transformation frameworks for cross-team adoption.
• Build observability into data pipelines with monitoring, alerting, and dashboards for latency, drift, and failures.
• Implement data quality checks, schema validation, and governance controls.
• Ensure pipelines meet enterprise standards for compliance, audit, and resilience.
• Mentor engineers, conduct design/code reviews, and enforce data engineering best practices.
• Collaborate with infra, product, and governance teams to align on priorities and safe adoption of AI.
• Drive delivery of high-profile AI applications by balancing execution speed with system reliability.

Requirements

• 7+ years of data engineering experience, with at least 2 years in a lead or senior technical role.
• Experience building and scaling streaming data pipelines in large-scale, distributed environments.
• Strong skills in Python, Java and SQL with expert level skill in either Python or Java.
• Proven experience building streaming data pipelines (e.g., Kafka, Flink, Spark, Kinesis).
• Experience with embedding pipelines and vector stores (e.g., Pinecone, Weaviate, FAISS, pgvector).
• Strong knowledge of data modeling, storage optimization, and retrieval patterns for large-scale systems.
• Hands-on experience with workflow orchestration tools (Airflow, Dagster, etc.).
• Strong collaboration and communication skills, able to partner across AI engineering, infra, and product teams.

Benefits

• A bonus and/or long-term incentive units may be provided as part of the compensation package
• Full range of medical, financial, and/or other benefits, dependent on the level and position offered