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Salary: USD 190,000 - 250,000 / annual
Eve is an AI-native legal technology platform serving plaintiff law firms. The company has achieved product-market fit with 1000+ law firms, raised $160M from top-tier investors (Spark Capital, A16z, Menlo Ventures, Lightspeed), and is growing 2X revenue quarter-over-quarter. The team includes engineers and operators from Scale, Meta, Airbnb, Cruise, Square, Rubrik, and Lyft, and the company collaborates directly with OpenAI and Anthropic on AI workflows.
You will own two domains: product analytics and customer success analytics. Your mission is to establish single, governed definitions for all product and customer metrics—weekly active usage, adoption depth by feature, launch performance, customer health, onboarding time-to-value, net revenue retention, churn, and expansion. Today these metrics are fragmented across tools and stakeholders; you will unify them.
Your stakeholders are Product (tracking adoption and launch health) and Customer Success / RevOps (managing health scores, renewal risk, and expansion). The most valuable work sits at the intersection: usage behavior is the strongest early signal of renewal, but nobody can see it today.
You will report to the Head of Data Engineering, who reports directly to the CEO. Data is a first-class function at Eve.
Key responsibilities:
- Build models for product usage, feature adoption, and launch performance (WAU, MAU, adoption depth, cohort analysis).
- Build models for customer success: health scoring, onboarding time-to-value, renewal risk, expansion, sourced from CS platforms, support systems, and lifecycle data alongside product usage.
- Own NRR, churn, and expansion as governed definitions, reconciling with Finance's revenue reporting.
- Design semantic models connecting usage behavior to customer outcomes so CS can identify which behaviors predict renewal.
- Build on the foundational layer owned by data engineers (source-to-staging models and conformed dimensions).
- Work within the team's certification framework and modeling standards, helping shape them as they evolve.
- Partner with Product on event taxonomy and tracking plans to ensure analytics rest on owned instrumentation.
- Instrument models with alerting so failures and drift surface before stakeholders discover them.
- Maintain documentation of models, metrics, and definitions.
- Partner with stakeholders to turn open questions into durable models rather than one-off answers.
- Work with analysts in your domains, designing and reviewing their contributions.
- Stand up internal AI agents and data-grounded tools that give stakeholders direct, trustworthy answers without waiting on tickets.
- Build skills and agents that speed your own work and contribute generalizable ones back to the team.
- Build patterns in Omni and Hex that stakeholders can use independently.
- Scope requirements and carry projects through their full lifecycle.
Requirements:
- 5+ years in analytics engineering, owning projects end to end.
- Strong SQL, data modeling, and transformation expertise with advanced dbt skills: modeling patterns, macros, packages, testing, and SCD table building from multiple sources.
- Working knowledge of the modern stack: Snowflake, dbt, and a semantic or BI layer (Omni or Hex).
- Experience modeling product usage and event data, including instrumentation (Amplitude, Mixpanel, Pendo, or similar).
- Experience modeling customer lifecycle and retention data: health scoring, renewal risk, NRR, churn, expansion from CS platforms and support systems.
- Experience designing semantic models or metric layers for human and AI consumption.
- Demonstrated ability to turn ambiguous stakeholder questions into models that continue answering after the original asker moves on.
- Proficiency with AI-assisted development (Claude Code), including agentic pipeline design, skill-based workflows, and MCP server integration.
- Strong communication skills: ability to distill technical solutions into business terms and comfort building where playbooks don't exist.
Nice to haves:
- Experience in regulated or high-sensitivity data environments (legal, healthcare, financial services).
- Experience where product usage data drove retention or expansion motions, not just dashboards.
- B2B SaaS background, especially selling to SMBs or professional services firms.
