Senior Analytics Engineer

Role overview A senior analytics engineering role owning two interconnected domains: product analytics and customer success. The mission is to make every product and customer metric resolve to a single, governed definition, from weekly active usage and feature adoption to customer health, net revenue retention, churn, and expansion. The work sits at the intersection of product and customer success teams, where usage behavior becomes the leading signal for renewal risk and growth.

Responsibilities - Build and maintain data models tracking product usage, feature adoption, launch performance, and behavior-based metrics such as WAU, MAU, adoption depth, and cohort analysis - Develop models powering customer success workflows: customer health, onboarding time-to-value, renewal risk, and expansion, drawing from CS platforms, support systems, and lifecycle data - Govern definitions for NRR, churn, and expansion, reconciling them with how finance reports revenue movement - Design semantic layers that connect usage behavior to customer outcomes, enabling CS teams to identify renewal predictors - Partner with product teams on event taxonomy and instrumentation plans to ensure governed analytics - Build internal AI agents and data-grounded tools so stakeholders can answer their own questions without ticket queues - Document owned models, metrics, and definitions, and instrument them with alerting for failures and drift - Mentor and review analysts contributing models in the same domains, and contribute reusable skills to the team's shared library

Requirements - 5+ years of analytics engineering experience owning projects end to end - Strong SQL, data modeling, and transformation skills, with deep dbt expertise including advanced patterns, macros, packages, and testing, plus experience building SCD tables - Working knowledge of a modern stack including Snowflake, dbt, and a semantic or BI layer such as Omni or Hex - Hands-on experience modeling product usage and event data with instrumentation tools like Amplitude, Mixpanel, or Pendo - Experience modeling customer lifecycle and retention data, including health scoring, renewal risk, NRR, churn, and expansion - Experience designing semantic models or metric layers for both human and AI consumption - Proficiency with AI-assisted development tools such as Claude Code, including agentic pipeline design and MCP integrations - Strong communication skills, including the ability to translate technical work into business terms and operate where no playbook exists

Nice to have - Experience in regulated or high-sensitivity data environments such as legal, healthcare, or financial services - Background where product usage data directly drove a retention or expansion motion rather than just dashboarding - B2B SaaS experience, particularly selling to small and mid-sized businesses or professional services firms

Benefits and work setup - Competitive salary and equity - 401(k) program with employer matching - Health, dental, vision, and life insurance - Short-term and long-term disability coverage - Commuter benefits for in-office employees - Workplace setup reimbursement - Telecomm stipend - Flexible time off plus holidays - Quarterly team gatherings - Autonomous work environment