Software Engineer - Data Infrastructure

About Assembled

Great customer support requires human agents and AI in perfect balance, and Assembled is the only unified platform that orchestrates both at scale. Companies like Canva, Etsy, and Robinhood use Assembled to coordinate their entire support operation — in-house agents, BPOs, and AI — in a single operating system. With AI Agents that resolve cases end-to-end, AI Copilot for agent assistance, and AI-powered workforce management that optimizes both human and AI capacity, Assembled helps teams deliver faster, better service while making smarter decisions about how to staff and automate. Backed by $70M from NEA, Emergence Capital, and Stripe, we're building the platform that makes AI and human collaboration actually work.

The Role

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.

In this role you'll:

• 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

You may be a good fit if you:

• 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

Nice to have (but not required):

• Experience with semantic layer tools like Cube, MetricFlow, or Looker’s LookML

• Familiarity with analytics-focused software engineering (e.g., event tracking, funnel analysis, experiment platforms)

• Experience modernizing legacy data systems or planning large-scale migrations

• Experience collaborating cross-functionally with AI/ML teams or product managers focused on AI systems

Our U.S. benefits

• Generous medical, dental, and vision benefits

• Paid company holidays, sick time, and unlimited time off

• Monthly credits to spend on each: professional development, general wellness, Assembled customers, and commuting

• Paid parental leave

• Hybrid work model with catered lunches everyday (M-F), snacks, and beverages in our SF & NY offices

• 401(k) plan enrollment