Staff AI Data Engineer

Department: Engineering

Location: Brazil, Argentina, Mexico

Employment Type: FullTime

Our Mission and Opportunity

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

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.

Who You Are

You are a Staff level full-stack builder operating at the intersection of AI systems and data architecture. You are both AI-native and product-minded. You love taking an ambiguous customer problem, turning it into a clear plan, and shipping a real end-to-end experience that moves a meaningful outcome. You care about craft and trust in what you ship, and you leave behind reusable building blocks so the next team can move even 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 and build a durable job execution system for agent workflows, including retries, explicit budgets, idempotency, and monitoring.

• Build evidence-first AI pipelines that produce structured outputs with provenance and uncertainty handling, and that store artifacts and evidence rather than overwriting truth.

• Design data foundations that allow AI to stitch together longitudinal operational signals across domains (customers, prospects, interactions, transcripts, product/ops/billing/support signals) into reliable workflows.

• Create shared abstractions and tooling for AI and data systems: tool interfaces, logging, cost tracking, evaluation harnesses, and reusable workflow components.

• Establish data contracts, SLAs, observability, and auditability practices that increase trust in both data and AI outputs.

• Partner with internal teams as customers to define success metrics, design workflow delivery surfaces, and iterate quickly 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

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.

• Hands-on experience with LLM systems in real applications, including tool use, retrieval-style patterns, evaluation, and monitoring.

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

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

Nice-to-Haves

• Experience designing lakehouse or warehouse architectures that support both analytics and AI workloads, with thoughtful cost and performance tradeoffs.

• Hands-on experience implementing vector indexing, embedding pipelines, or hybrid structured + semantic retrieval systems in production.

• Experience building event-driven or real-time data architectures that support operational intelligence, not just batch reporting.

• Background in vertical SaaS, CRM, or operations-heavy domains where operational data is central to product differentiation.

• Experience building internal data platforms or shared services adopted across multiple engineering teams.

• Experience defining data governance frameworks, PII handling standards, and auditability patterns in AI-enabled systems.

• Demonstrated ability to influence technical strategy at Staff or Principal level across organizational boundaries.

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 [email protected]. Thank you for helping us keep our applicant community safe.