Sr Data Engineer
$138,900–$203,900 year
On-site · San Francisco, California, United States or Seattle, Washington, United States
Job Summary
Senior Data Engineer for Disney Ads Platforms who will design, build, and scale the data foundations powering AI adoption across Ad Technology. Responsibilities include building high-performance streaming and batch data pipelines, embedding generation workflows, vector store integrations, and retrieval pipelines to support semantic search and RAG-powered AI assistants. Develop AI-optimized data models, integrate pipelines with shared AI platform services, and create reusable ingestion/processing components. Establish end-to-end observability, data quality validation, schema evolution safeguards, and governance controls. Drive full development lifecycle from prototyping to deployment, mentor junior engineers, collaborate with infra and product teams, and influence system design with hands-on leadership.
Required Qualifications
- Bachelor's degree in computer science or a related quantitative field; or equivalent practical experience
- 5+ years of data engineering experience, with at least 1 year 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
- Familiarity with testing, monitoring, and automation for data pipelines
- Experience with LangChain, LangGraph (preferred)
- Experience with observability stacks (Datadog, Prometheus) (preferred)
- Background in governance and compliance practices for enterprise data platforms (preferred)
- Experience building data frameworks, registries, or accelerators adopted by multiple teams (preferred)
- Experience ensuring data security, lineage, and auditability in enterprise data environments (preferred)
- Experience with Python, Java, Databricks, LangChain, Vector Stores(Pinecone, Weaviate, FAISS, pgvector), SQL, AWS big data stack (S3, Glue, MWAA)
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