Senior Software Engineer - AI Data & Analytics

Overview

Our AI agents generate an enormous amount of data: every task they execute, every email they process, every call they handle, every workflow they complete. Today that data powers the product. Tomorrow it should power the business—and become a product in its own right. This role owns that transformation.

Responsibilities

• Build the data platform for analytics: design schemas, aggregation layers, and pipelines that turn operational data into a reliable source of truth serving customers, product, and finance.
• Ship customer-facing analytics: build the dashboards, APIs, and data contracts that give customers live visibility into what our AI is doing for them—usage and credit consumption, performance metrics, community-level insights (Next.js front end, Postgres-backed services).
• Make consumption-based pricing real: build event-level usage metering and attribution with audit-grade correctness, plus the reconciliation pipeline that connects product data to billing and finance reporting.
• Build forecasting and predictive models: usage run-rate projections, sentiment analysis for our customers’ customers based on community communications, and other applied data applications that provide unique insights into our customers.
• Own data correctness and reliability: data quality testing, pipeline observability, anomaly detection, and the engineering rigor that makes numbers trustworthy enough to bill against and report to boards.
• Raise engineering standards: drive code quality, testing strategy, documentation, and mentoring as the data and analytics surface area grows.

Requirements

• Experience: 5+ years building backend or data systems for production SaaS.
• Data engineering depth: strong relational modeling (Postgres), pipelines, aggregation systems, indexing, query optimization, and an instinct for data correctness.
• Applied data science fluency: comfort with statistics, forecasting, and ML techniques—and the judgment to know when a regression beats a neural net.
• API design excellence: proven ability to design clear, evolvable APIs and data contracts across services and customer-facing surfaces.
• Full-stack interest: you don't need to be a front-end specialist, but you're excited to take an insight all the way to the screen (React/Next.js).
• High pace + high quality: you thrive in fast-moving environments without sacrificing reliability or security.
• Curiosity and ownership: you enjoy ambiguous problems, learn quickly, and take systems end-to-end (build → ship → operate).

Nice to Have

• Usage-based billing: experience with metering, credit ledgers, rating engines, or revenue reconciliation.
• LLM experience: building product with LLMs (prompting, tool calling, evals, cost analytics), or analyzing LLM-generated data at scale (sentiment, classification, extraction).
• Modern data stack: dbt, warehouses/lakehouses, event streaming, orchestration tools.
• Data visualization: a track record of dashboards or analytics products that customers actually used.