Sr Data Engineer

Job Summary The Ads Data team, part of Disney’s Ad Platforms organization, is on a mission to transform the advertising landscape across TV and streaming video through the power of data and AI. We design and build innovative solutions that measure and optimize the entire advertising lifecycle — from planning and activation to measurement and insights. At the heart of our work is a highly collaborative, cross‑functional team that delivers end‑to‑end solutions across a wide spectrum of technologies, including machine learning, big data, microservices, and data visualization. We are hiring a Senior Data Engineer to design, build, and scale the data foundations that power AI adoption across Ad Technology. In this role, you will own how data flows into AI‑ready and optimized stores, from streaming pipelines and embedding pipelines to vector stores and monitoring systems. You’ll partner closely with the AI Core Engineering team to enable shared agents, registries, and developer tools with robust, reliable, real‑time data. As a Senior Engineer, you will design and implement robust data engineering solutions, mentor junior team members, and build resilient pipelines that support high‑profile AI applications. You will play a key role in developing reliable, real‑time data foundations, enabling faster and safer deployments across Disney Ad Tech. This role is ideal for someone who combines strong technical expertise with a passion for leadership, system design, and delivering real business impact through data platforms. If you thrive in a fast‑paced, highly collaborative environment and are excited about building the foundation for the future of advertising, we’d love to hear from you. Responsibilities and Duties of the Role Data Architecture & Pipeline Development Build and maintain high‑performance streaming and batch data pipelines that power AI applications, ensuring reliable low‑latency ingestion and high‑throughput processing. Implement and extend embedding generation workflows, vector store integrations, and retrieval pipelines supporting semantic search, RAG systems, and AI assistants. Develop and optimize scalable storage and retrieval patterns, focusing on cost‑efficient architecture and smooth production performance. AI Data Foundations & Integration Implement AI‑optimized data models and storage patterns that align with broader enterprise architecture and platform requirements. Integrate pipelines with shared AI platform services (agent frameworks, registries, feature stores), ensuring clean, versioned, and reliable data delivery. Build reusable ingestion, transformation, and data processing components that streamline adoption across engineering teams. Monitoring, Quality & Reliability Embed end‑to‑end observability into data systems, including metrics, structured logging, automated alerts, drift detection, and failure analysis. Implement robust data quality validation, schema evolution safeguards, and governance/compliance controls. Ensure deployed pipelines meet high standards for reliability, recoverability, auditability, and long‑term maintenance. Execution, Collaboration & Leadership Drive execution by owning the full development lifecycle: prototyping, implementation, testing, deployment, optimization, and documentation. Collaborate closely with infrastructure, ML engineering, product, and governance teams to deliver production‑ready AI capabilities. Lead by example through strong execution, high‑quality code, and proactive problem solving. Influence design direction through technical proposals and hands‑on delivery rather than formal ownership of standards. Required Education, Experience/Skills/Training Basic Qualifications 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, able to partner across AI engineering, infra, and product teams. Familiarity with testing, monitoring, and automation for data pipelines. Preferred Qualifications Experience integrating AI‑ready data stores with LLM orchestration frameworks (LangChain, LangGraph, etc.). Knowledge of observability and monitoring stacks (Datadog, Prometheus, or equivalent). Background in governance and compliance practices for enterprise data platforms. Experience building data frameworks, registries, or accelerators adopted by multiple teams. Experience ensuring data security, lineage, and auditability in enterprise data environments. Skilled at writing design documentation, driving system architecture reviews, and influencing data engineering culture. Experience with: Python, Java, Databricks, LangChain, Vector Stores (Pinecone, Weaviate, FAISS, pgvector), SQL, AWS big data tech stack (like S3, Glue, MWAA) Required Education Bachelor or above in computer science or a related quantitative field; or equivalent practical experience demonstrating advanced technical expertise The hiring range for this position in Glendale, CA is $138,900 - $186,200 per year, in San Francisco, CA is $152,100 - $203,900 per year and in Seattle, WA is $145,400 - $195,000 per year. The base pay actually offered will take into account internal equity and also may vary depending on the candidate’s geographic region, job‑related knowledge, skills, and experience among other factors. A bonus and/or long‑term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered. #J-18808-Ljbffr Dormont Manufacturing Co