Senior Analytics Engineer, Trellis

• Success in this role requires strong ownership of the analytics layer, deep technical expertise in data modeling and tooling, and the ability to balance speed, quality, and scalability
• Clear communication, priority management, and the ability to lead data projects from idea through execution are essential
• 5+ years of experience in data, including 3+ years owning data modeling (Analytics Engineer, Data Engineer)
• Expert in SQL, capable of writing efficient, production-grade queries
• Deep knowledge of DBT, including macros, packages, source freshness, incremental builds, and custom tests
• LookML development and ownership background
• Ability to translate complex business concepts into clear, actionable data models
• High Autonomy & Execution: Demonstrated capacity to operate with high autonomy, follow through on unowned problems, and thrive in a fast-paced, scrappy startup environment
• Communication: Clear, concise, and professional written and verbal communication for both internal alignment and external partner management

Desirable

• We are seeking a Senior Analytics Engineer to own, evolve, and scale the analytics foundation that enables data-driven decision-making across our insurance marketplace
• This role is central to ensuring that our product, data science, and agency teams can rely on fast, accurate, and well-modeled data to operate and experiment effectively
• You will report directly into Jaren Carlson, Head of Analytics Engineering
• Model Complex Business Processes in Data: Design and refine data models and business definitions that accurately represent product and agency operations, leveraging both existing data and new abstractions as needed
• Build Data Quality & Alerting Systems: Establish robust data quality checks and proactive alerting to surface inconsistencies and help the insurance agency resolve issues quickly
• Ensure Fast, Reliable Analytics: Own query performance and warehouse optimization to deliver timely, dependable insights while optimizing costs
• Drive Impact Through Data Partnerships: Work closely with data science, product managers, and engineering managers to both produce and consume high-impact data that supports product development, experimentation, and operational decision-making
• Streamline Data Development : Improve workflows and implement best practices to maximize productivity and consistency across data tooling (Looker, DBT)