Assembled builds the infrastructure that underpins exceptional customer support, empowering companies like CashApp, Etsy, and Robinhood to deliver faster, better service at scale. With solutions for workforce management, BPO collaboration, and AI-powered issue resolution, Assembled simplifies the complexities of modern support operations by uniting in-house, outsourced, and AI-powered agents in a single operating system. Backed by $70M in funding from NEA, Emergence Capital, and Stripe, and driven by a team of experts passionate about problem-solving, we're at the forefront of support operations technology. We're looking for an experienced software engineer to help shape the foundation of Assembled's data systems. You'll join our Data Infrastructure team, a close partner to both our Core Infrastructure and AI Infrastructure teams, to own how data is modeled, stored, and served across the company. This work powers everything from customer-facing dashboards to internal analytics and AI-driven product features. We're currently rebuilding our metrics infrastructure from the ground up. Our legacy Go-based system made it difficult to scale, maintain, and trust the metrics we expose. We're building a new analytics stack that enables fast, reliable metric queries and simplifies the development of new reports. You'll be joining at a pivotal moment-early prototypes are in place, and we're working toward a full-scale production rollout and long-term migration. The team also plays a central role in the development of Assembled's AI platform, Assist. As we unify our WFM and AI products into a single Human + AI experience, the Data Infrastructure team is responsible for the analytics that help customers understand how Assist is adopted, how it impacts performance, and where to optimize. You'll collaborate closely with the Assist team to build robust data models and systems that support this functionality. One of the more unique challenges in this role is that our data infrastructure doesn't just support internal analytics-it powers customer-facing product experiences. While some outputs are traditional dashboards, others require near real-time responsiveness. As a result, our stack must support both large-scale analytical queries and low-latency, user-triggered interactions-capabilities that most analytics systems are not architected to handle simultaneously. We're building a unified system that can do both, without introducing mismatched data or duplicated definitions.