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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr Product Software Engineer - Full Stack - **Company:** The Walt Disney Company - **Location:** Seattle, WA, United States - **Experience:** Expert - **Salary:** $150,900.0 - $202,400.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), JavaScript (Programming Language), Web Accessibility, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Code Review, Information Systems, Data Integration, Data Security, Data Visualization, Data Warehousing, Java Web Services, Python (Programming Language), Node.Js, Software Product Management, Software Engineering, SQL Databases, TypeScript, Web Content Accessibility Guidelines, Cloud Platform System, Test-Driven Development (TDD), ReactJS, System Availability, Large Language Models, Snowflake, Caching, AI Coding Agents, Backend, Information Technology, Low Latency, Data Analytics, Data Management, Looker Analytics, Api Management, Databricks - **Published:** November 7, 2026 - **Apply:** https://dejobs.org/x/x/A79F955EC002489A9F7F3B32BF3218FC/job/ ## About the Role * 5+ years building scalable, performant full-stack web applications serving large user and request volumes. * Strong command of TypeScript and JavaScript across both React front ends and Node.js services, plus working proficiency in at least one other back-end language such as Python or Java. * Experience designing and implementing APIs and server-side data access, in collaboration with service, back-end, and data teams. * Comfort with SQL and relational or warehouse data models. * Experience building data-heavy product surfaces, such as dashboards, reporting, or internal analytics tools, where both correctness and latency matter. * Solid performance-engineering fundamentals across the stack (query and API latency, caching, data fetching, rendering) for data-heavy or high-traffic systems. * Track record of owning features or components beginning to end and driving complex problems to root cause and resolution. * A regular practitioner of agentic software development: AI coding agents and agent-driven workflows are a core part of how you build., * Hands-on experience building with LLMs or agents in a production product, for example natural-language querying, agent-driven insight generation, or prompt-to-dashboard. * Production experience with Python and/or Java services, data tooling, or integrations at meaningful scale. * Shipped production applications with high availability and throughput on a cloud platform (e.g., AWS), including the operational side. * Familiarity with BI and data-warehouse stacks (e.g., Looker/LookML, Snowflake, Databricks) and partnering with data and analytics teams. * Experience with data visualization at scale: charting, large and high-cardinality datasets, interactive and filterable dashboards. * Experience with configuration-driven or schema-driven systems (product defined as data or "as code"). * Working understanding of web accessibility (WCAG) fundamentals. * Broad knowledge of web and back-end platform standards and emerging technologies. Required Education * Bachelor's degree in Computer Science, Information Systems, Software or Electrical Engineering, a comparable field, and/or equivalent work experience. ## Description Pixie and Pulse are the analytics platform that people across Disney+, Hulu, and ESPN use to understand the business, from an analyst checking a metric to executives deciding what to do next. You'll be a senior engineer building it: the services and integration layer that turn our BI and data-warehouse stack into trustworthy metrics, the APIs on top of them, and the React front end where that work becomes dashboards leaders act on every day. The platform is purpose-built so data-rich dashboards load fast, stay correct, and can be assembled and changed through configuration rather than one-off code. Most of the job is core product and platform engineering: new metric and dashboard surfaces, the config and schema system behind them, the data integrations that feed them, performance under dashboard-shaped traffic, and the correctness guarantees that let an executive trust a number. Part of the job is AI. More people now start from a natural-language question and let agents help assemble, explain, and explore the data, with people in the loop on what ships, and you'll help build those surfaces alongside everything else. You'll own significant features and components across that stack, taking them from ambiguous problem to production and ongoing operation. You'll make the design decisions inside the areas you own and contribute to the broader architecture alongside the initiative's lead and principal engineers, working at the seam between product, design, and data and analytics partners. Responsibilities and Duties of the Role: Build across the stack Build the full path: back-end services in Node.js, Python, or Java, the APIs and data-integration layer over our BI and warehouse stack, and the React front end on top of them. Own features end to end: partner with product, design, and data teams to turn an ambiguous problem into a clear design, then build, test, ship, and operate it. Evolve a configuration and schema-driven system that lets new dashboards and metrics ship through config and CI validation rather than bespoke code, treating product surfaces as data. Metrics, data integration, and trust Build and extend the metric and dashboard surfaces the business runs on, from the query and semantic layer through to what renders on screen. Make correctness a property of the system, not a manual check: consistent metric definitions, validation in CI, and the observability to catch a number that has quietly gone wrong before a stakeholder does. Design and technical judgment Make and document the design decisions in your area (service boundaries, API contracts, data models, caching, state, rendering), and bring the ones with wider blast radius to the team's lead and principal engineers for review. Drive complex problems to root cause wherever they live, from a slow warehouse query to a client-side rendering bottleneck. Performance and scale Engineer for dashboard-shaped traffic on both sides of the wire: query and service latency, caching layers, request batching, and progressive and streaming rendering, so complex, high-cardinality views load fast and scale to a large concurrent audience. AI-assisted analytics Contribute to the AI surfaces of the platform: natural-language and AI-assisted dashboard authoring, agent-accessible endpoints, and AI-generated insights with human-in-the-loop review. Help build the foundations these depend on, which are useful either way: a semantic, machine-readable representation of metrics and surfaces, plus the evaluation and guardrails that keep generated output trustworthy. Quality and craft Hold a high bar through testing (including test-driven development where it fits), code review, observability, and accessible, resilient UI. See work through from specification to production and ongoing operation, including the operational reality of what you ship. Collaboration and mentorship Mentor early-career and mid-level engineers through code review, pairing, and knowledge sharing. Help advance the team's practices and standards, including agentic engineering workflows that raise how fast and how well the team ships.