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Brightwheel is seeking a Staff Data Engineer and technical lead on our Data Engineering team. As a Staff Data Engineer at brightwheel, you will architect and drive the evolution of our data platform, partnering with technical leadership to shape our data and AI strategy. You will design and scale sophisticated data pipelines processing billions of records across diverse systems, powering analytics for internal teams, customer-facing insights, and AI/ML capabilities that differentiate our product.
Job Responsibility:
Architect and lead the evolution of our modern data platform, driving technical decisions on tooling, infrastructure patterns, and scalability strategies
Design and build production LLM pipelines and infrastructure that power intelligent operations
Own end-to-end data acquisition and integration architecture across diverse sources
Create shared abstractions and tooling for AI
Shape our data and system architecture so AI can safely stitch together longitudinal signals across product, billing, support, and operations
Lead by example in AI-augmented engineering, using AI to multiply your own speed, mentoring L2/L3 engineers
Mentor and influence engineering culture, conducting design reviews, providing technical guidance to engineers across the organization
Requirements:
6+ years of work experience as a data engineer, backend engineer, full stack or DevOps engineer with strong proficiency in Python and modern data engineering practices
Applied AI impact at scale: Proven track record of shipping AI / LLM-powered features into production with clear, measurable impact on key metrics
Hands-on experience with large language models (LLMs) in real applications, including prompt and tool design, retrieval-style patterns, and evaluation and monitoring in production
Strong computer science fundamentals (e.g., data structures, algorithms, and systems design) and a generalist mindset
Experience designing, developing, and deploying ML/LLM/AI pipelines in production environments, including experience with model serving, feature engineering, and MLOps practices
Expert-level understanding of distributed data processing technologies and their internals
Proven track record of independently architecting scalable data solutions
Nice to have:
Proven track record of technical leadership, including mentoring senior engineers, driving engineering standards and best practices, and influencing data platform strategy across the organization
Hands-on experience architecting federated query engines over lakehouse platforms
Deep expertise building orchestration platforms with Airflow
Advanced experience with serverless and event-driven architectures
Experience building customer-facing embedded analytics solutions