brightwheel - Staff AI Product Builder, Data Engineering

• Driven by outcomes: You care about helping operations, GTM, product, and engineering teams move faster, make higher-quality data-driven decisions, and build AI-powered workflows with confidence. You measure success in reduced friction, improved signal reliability, and meaningful business impact — not just infrastructure shipped. • Driven by outcomes: • AI-native. You understand how LLMs interpret data and design retrieval, evaluation, and observability into systems from the start. • AI-native • A product-driving technical leader. You define what data should exist, how it should be structured, and how AI should safely interact with it to drive workflow improvements. • A product-driving technical leader • Deep in data modeling and system design. You design schemas, contracts, and storage strategies that enable AI reasoning across domains, not just analytics queries. • Deep in data modeling and system design • Thoughtful about safety and privacy. You build AI-aware data systems with governance, access control, and auditability as first-class concerns. • In this role, you will own AI-powered improvements in core brightwheel workflows end-to-end, with particular emphasis on the data foundation that enables those workflows. You will: • Ship "virtual employee" workflows that do real work before humans engage: research, verification, prioritization, deduplication, and prep artifacts that cite evidence and flag unknowns. • Design the data foundations that let AI stitch together longitudinal operational signals across domains (customers, prospects, interactions, transcripts, product, ops, billing, support) into reliable workflows. Build evidence-first pipelines that produce structured outputs with provenance and uncertainty handling, and that store artifacts rather than overwriting truth. • Build a durable job execution system for agent workflows: retries, explicit budgets, idempotency, and monitoring. • Create shared abstractions for AI and data systems: tool interfaces, logging, cost tracking, evaluation harnesses, data contracts, SLAs, and reusable workflow components that increase trust in both data and AI outputs. • Partner with internal teams as customers. Define success metrics with them, design workflow delivery surfaces, and iterate based on adoption and impact. • Lead by example in AI-augmented engineering, using AI tools to increase velocity while maintaining architectural rigor. • What You’ve Done • What You’ve Done • We are open to a variety of backgrounds, but you likely bring: • 5+ years of professional engineering experience with clear ownership of production systems from design doc through launch and iteration. • A track record of shipping AI-powered workflows to production with measurable impact, including hands-on experience with LLM tool use, retrieval patterns, evaluation, and monitoring. • Experience operating AI systems in production: evaluation harnesses, rollout strategies, and monitoring that ties system health to output quality. • Experience designing data platforms for operational use cases: canonical models, identity resolution and deduplication, and governance patterns that support safe downstream consumption. • Experience designing reliable workflow systems: job orchestration, backfills and retries, observability, and cost/performance tradeoffs. • Demonstrated ability to influence technical strategy across organizational boundaries. • Nice-to-haves: • Lakehouse or warehouse architectures that support both analytics and AI workloads. • Vector indexing, embedding pipelines, or hybrid structured + semantic retrieval in production. • Event-driven or real-time data architectures for operational intelligence, not just batch reporting. • Vertical SaaS, CRM, or operations-heavy domains where operational data is central to product differentiation. • Internal data platforms or shared services adopted across multiple engineering teams. • Data governance frameworks, PII handling standards, and auditability patterns in AI-enabled systems. • TechnologyData foundations: relational databases and operational data platforms; canonical entity modeling; identity resolution/deduplication; data contracts and SLAs. • Technology • Workflow execution: job queues, schedulers, durable retries, and event-driven systems for bounded, measurable work. • AI systems: hosted LLMs, tool calling, retrieval patterns, and evaluation/monitoring tooling. • Observability and governance: logging standards, lineage/traceability patterns, access controls, privacy-aware designs, and auditability. • We value architectural judgment over attachment to specific tools. The right candidate can reason about tradeoffs across reliability, correctness, latency, and cost in AI-native systems.