Staff AI Product Builder, Data Engineering

Role Description

You're a Staff-level full-stack builder operating at the intersection of AI systems and data architecture. You're AI-native: you understand how LLMs interpret data, and you design retrieval, evaluation, and observability into systems from the start. You love turning an ambiguous customer problem into a clear plan and shipping an end-to-end experience that moves a meaningful outcome. You care about craft and the trust of what you ship, and you leave behind reusable building blocks so the next team can move faster.

You’ll succeed in this role if you are:

• 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.
• AI-native: You understand how LLMs interpret data and design retrieval, evaluation, and observability into systems from the start.
• 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.
• Deep in data modeling and system design: You design schemas, contracts, and storage strategies that enable AI reasoning across domains, not just analytics queries.
• Thoughtful about safety and privacy: You build AI-aware data systems with governance, access control, and auditability as first-class concerns.

What You’ll Do

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.

Qualifications

• 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.

Technology

• Data foundations: relational databases and operational data platforms; canonical entity modeling; identity resolution/deduplication; data contracts and SLAs.
• 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.