Brisk Teaching provides an AI extension that integrates with LMS and Google Workspace to automate teachers' routine tasks: content creation, grading and authentication, personalized tutoring, and adaptive student activities.
ABOUT US: Brisk Teaching is the leading AI platform for K-12 educators, empowering teachers to deliver personalized, curriculum-aligned instruction at scale. Our Classroom Intelligence tools connect teachers, students, and curriculum—making meaningful learning happen in classrooms every day. We believe the best technology is cutting-edge, respects teachers’ time, integrates into real classrooms, and helps educators focus on what matters most. Since launching, Brisk has experienced rapid growth. Over 2 million teachers use our platform, and we partner with districts and schools nationwide to transform how educators teach and how students learn. We're backed by leading venture and edtech investors including Bessemer Venture Partners (Shopify, LinkedIn, Anthropic, Perplexity) and Owl Ventures (Newsela, Quizlet, MasterClass), with a team spanning top AI companies and real classroom experience. We're a fast-moving, collaborative team driven by a shared commitment to educator impact and product excellence. Our values—urgency, ownership, and focus—shape how we build and who we are. THE OPPORTUNITY: We've already started building something we think fundamentally rethinks how data science gets done at a company. We have an internal data agent that can query our warehouse, interpret results, and surface insights to stakeholders directly. It works. People use it. And it's changed how our team operates day to day. Now we need someone to take it further. As Brisk's product and organization grow, the systems underneath need to grow with them: a more robust semantic layer, better tooling for experimentation, more sophisticated automation across the data lifecycle, and the strategic thinking to decide where AI amplifies the team's work and where it replaces manual processes entirely. This role is equal parts strategy and execution. You'll own the vision for how data drives decisions across Brisk, scale the AI-native workflows we've started, and build the systems that turn a small data team into one that operates like a much larger org. You'll report directly to leadership and have a seat at the table on product strategy, go-to-market, and company priorities. YOU WILL: - Set the data strategy: Define what we measure, how we measure it, and what "good" looks like. Establish the metrics architecture that connects product usage, retention, monetization, and growth into a coherent picture, and make sure every team at Brisk is oriented around it. - Turn the data team into a product team: Build internal data products that stakeholders across the company actually use daily. Replace ad-hoc requests with self-serve AI interfaces, automated reports, and tools that make data accessible to non-technical teammates. - Make the warehouse AI-readable: Build the semantic layer, documentation, and context infrastructure that lets both humans and AI systems query Brisk's data accurately. A well-documented warehouse is the foundation for every AI-powered workflow that follows. - Build AI into the data lifecycle: Identify where AI agents, automated pipelines, and LLM-powered tooling can replace manual work: dbt model generation, data quality monitoring, experiment analysis, insight delivery. Ship these systems into production. - Own product analytics and experimentation: Partner with Product, Engineering, and Design to design experiments, interpret results, and deliver the insights that shape what we build. Bring rigor to how we evaluate features and make ship/no-ship decisions. - Drive growth and business intelligence: Maintain and evolve dashboards and reporting for Sales, Marketing, Customer Success, and leadership. Ensure the metrics that matter are visible, trusted, and actionable. - Scale through systems, not headcount: Build infrastructure and AI-powered tooling that multiplies the team's output so a small data org can support a fast-growing company without scaling linearly. YOU HAVE: - 8+ years of experience in data science, analytics engineering, or a related role. You've operated across the full analytics lifecycle and seen what works (and what doesn't) at scale. - Comfort with ambiguity and speed: You've worked at a startup or similar environment where priorities shift fast, resources are limited, and scrappiness is a survival skill. You don't need a playbook to get started. - Strategic instincts: You identify which questions matter, not just answer the ones you're given. You've partnered with product and business leaders to shape roadmaps, define success metrics, and make hard tradeoffs. - Deep SQL and warehouse expertise: You think in SQL. You've built and maintained data models, designed schemas, and know what it takes to keep a warehouse clean, documented, and trustworthy. - Pipeline and infrastructure experience: You've built data pipelines and dealt with data quality issues firsthand. You understand the plumbing as well as the dashboards. - dbt experience: You've built and maintained production dbt models: transformations, testing, documentation. You understand how a well-structured dbt project makes everything downstream better. - Strong product sense: You've worked closely with product teams, you understand what drives user behavior in software (ideally in edtech or SaaS), and you have good instincts for what to measure and what to build. - Enough engineering chops to ship: You can build a working tool in Python, wrangle APIs, and write code that other people can maintain. You're not a SWE, but you're not afraid of production. - Genuine excitement about AI: You've been building with LLMs. You have opinions about which models are good at what, you've experimented with agents or RAG systems, and you believe AI is going to fundamentally change how data teams operate. - Builder mentality: You see a manual process and you can't help but automate it. You ship fast, iterate, and care more about impact than perfection. BONUS: - Experience with Snowflake.