Staff AI Product Builder, Data Engineering

Early education is one of the most important determinants of childhood outcomes, a critical support for working families, and a $175B market that remains underserved by modern technology. Brightwheel is the largest, fastest growing, and most loved platform in early ed, trusted by millions of educators and families every day. We are a three-time Cloud 100 company, backed by top investors including Addition, Bessemer, Emerson Collective, Lowercase Capital, Notable Capital, and Mark Cuban.

Our team is passionate, talented, and customer-focused. We embody our Leadership Principles in our work and culture. We are a distributed team with remote employees across every US time zone, as well as select offices in the US and internationally.

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.

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:

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  Ship "virtual employee" workflows that do real work before humans engage: research, verification, prioritization, deduplication, and prep artifacts that cite evidence and flag unknowns.

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  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.

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  Build a durable job execution system for agent workflows: retries, explicit budgets, idempotency, and monitoring.

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  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.

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  Partner with internal teams as customers. Define success metrics with them, design workflow delivery surfaces, and iterate based on adoption and impact.

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  Lead by example in AI-augmented engineering, using AI tools to increase velocity while maintaining architectural rigor.

We are open to a variety of backgrounds, but you likely bring:

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  5+ years of professional engineering experience with clear ownership of production systems from design doc through launch and iteration.

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  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.

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  Experience operating AI systems in production: evaluation harnesses, rollout strategies, and monitoring that ties system health to output quality.

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  Experience designing data platforms for operational use cases: canonical models, identity resolution and deduplication, and governance patterns that support safe downstream consumption.

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  Experience designing reliable workflow systems: job orchestration, backfills and retries, observability, and cost/performance tradeoffs.

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  Demonstrated ability to influence technical strategy across organizational boundaries.

• 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.

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.

Brightwheel is committed to creating a diverse and inclusive work environment and is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity, gender expression, sexual orientation, national origin, genetics, disability, age, or veteran status.

Protecting Our Applicants: Please be aware of recruiting scams impersonating Brightwheel. All legitimate communications come from @mybrightwheel.com addresses, and we never ask for payment or sensitive personal data as part of our hiring process. If you suspect fraudulent contact, reach out to security@mybrightwheel.com. Thank you for helping us keep our applicant community safe.