Software Engineer, Assembled

• Have experience working with modern data warehouses (e.g., Snowflake, BigQuery) and understand their performance characteristics
• Have built or maintained end-to-end ELT pipelines and are comfortable choosing the right level of precomputation
• Have designed or worked closely with a metrics or semantic layer, and understand how to define metrics that are consistent, queryable, and performant across reporting surfaces
• Are comfortable reasoning about systems tradeoffs—latency, cost, developer velocity, and reliability
• Take pride in building systems that are clear, maintainable, and empower others
• Have strong SQL fluency and are comfortable reading query plans, debugging slow queries, and optimizing for performance

Desirable

• 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
• Design and build systems that power both the storage and retrieval of analytical data
• Own the transformation layer that models data for fast, consistent metric queries
• Define and maintain the metrics layer that supports dashboards, exports, APIs, and internal tools
• Collaborate with product, infrastructure, and Assist teams to build rich reporting experiences—like helping customers measure ROI on AI adoption
• Manage scalable pipelines that move and transform production data for analysis
• Instrument observability into the data platform, including freshness, lineage, and correctness