Staff AI Product Builder, Data Engineering

brightwheel

12d•$154,000 - $237,000•Remote

About The Position

Brightwheel is seeking a Staff-level full-stack builder to operate at the intersection of AI systems and data architecture. This role is for someone who is AI-native, understands how LLMs interpret data, and designs retrieval, evaluation, and observability into systems from the start. The ideal candidate can turn ambiguous customer problems into clear plans and ship end-to-end experiences that drive meaningful outcomes. They care about craft, the trust of their work, and leaving behind reusable building blocks for future teams. The role is driven by outcomes, focusing on helping operations, GTM, product, and engineering teams move faster, make higher-quality data-driven decisions, and build AI-powered workflows with confidence. Success is measured by reduced friction, improved signal reliability, and meaningful business impact. The candidate will be a product-driving technical leader, defining data requirements, structure, and safe AI interaction for workflow improvements. They will be deep in data modeling and system design, creating schemas, contracts, and storage strategies for AI reasoning, and will be thoughtful about safety and privacy, building AI-aware data systems with governance, access control, and auditability as first-class concerns.

Requirements

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.
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.
Architectural judgment over attachment to specific tools, with the ability to reason about tradeoffs across reliability, correctness, latency, and cost in AI-native systems.

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.

Responsibilities

Own AI-powered improvements in core brightwheel workflows end-to-end, with particular emphasis on the data foundation that enables those workflows.
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, defining success metrics, designing workflow delivery surfaces, and iterating based on adoption and impact.
Lead by example in AI-augmented engineering, using AI tools to increase velocity while maintaining architectural rigor.