Senior Analytics Engineer
United StatesRemoteseniorQuick Summary
WAU and MAU, adoption depth by feature, launch cohort analysis Build the models CS runs on: customer health, onboarding and time-to-value, renewal risk, and expansion, sourced from your CS platform,
advanced modeling patterns, macros, packages, and testing. Experience building SCD tables from multiple sources Working knowledge of the modern stack: Snowflake, dbt,
Responsibilities
~1 min read- →Build the models that track product usage, feature adoption, and launch performance: WAU and MAU, adoption depth by feature, launch cohort analysis
- →Build the models CS runs on: customer health, onboarding and time-to-value, renewal risk, and expansion, sourced from your CS platform, support and ticket data, and lifecycle systems alongside product usage
- →Own NRR, churn, and expansion as governed definitions, built from the health, usage, and lifecycle signals underneath them, and reconcile with how Finance reports the same revenue movement
- →Design the semantic models connecting usage behavior to customer outcomes, so CS can see which behaviors actually predict renewal rather than guessing
- →Build on the foundational layer the data engineers own: source-to-staging models and conformed dimensions.
- →Work inside the certification framework and modeling standards the team sets, and help shape them as they evolve
- →Partner with Product on the event taxonomy and tracking plan, so product analytics rests on instrumentation someone actually owns
- →Instrument your models against the team's alerting so failures and drift surface before a stakeholder finds them
- →Maintain documentation of the models, metrics, and definitions you own
- Sit with stakeholders across Product and Customer Success to turn open questions into durable models rather than one-off answers
- Partner with analysts contributing models in your domains, designing with them where it helps and reviewing what they ship
- Stand up internal AI agents and data-grounded tools that give stakeholders a direct, trustworthy answer without waiting on a ticket
- Build the skills and agents that speed up your own work, and contribute the ones that generalize back to the team's shared library
- Build patterns in Omni and Hex that stakeholders can actually use on their own
- Scope requirements and carry projects through the full lifecycle
- 5+ years in analytics engineering, owning projects end to end
- Strong SQL, data modeling, and transformation, with dbt expertise: advanced modeling patterns, macros, packages, and testing. Experience building SCD tables from multiple sources
- Working knowledge of the modern stack: Snowflake, dbt, and a semantic or BI layer such as Omni or Hex
- Experience modeling product usage and event data, and the instrumentation behind it (Amplitude, Mixpanel, Pendo, or similar)
- Experience modeling customer lifecycle and retention data: health scoring, renewal risk, NRR, churn, and expansion, from CS platforms and support systems
- Experience designing semantic models or metric layers for human and AI consumption
- You've taken an ambiguous stakeholder question and turned it into a model that kept answering after the person who asked moved on
- Proficiency with AI-assisted development such as Claude Code, including agentic pipeline design and skill-based workflows, and comfort integrating tools via MCP servers
- Strong communication, including the ability to distill technical solutions into business terms, and comfort building where the playbook doesn't exist yet
Nice to Have
~1 min read- Experience in a regulated or high-sensitivity data environment (legal, healthcare, financial services)
- Experience where product usage data drove a retention or expansion motion, not just a dashboard
- B2B SaaS, especially selling to small and mid-sized businesses or professional services firms
What We Offer
~1 min read💰 Competitive Salary & Equity
💹 401(k) Program with Employer Matching
⚕️ Health, Dental, Vision and Life Insurance
🩼 Short Term and Long Term Disability
🚗 Commuter Benefits*
🧑💻 Autonomous Work Environment
🖥️ Workplace Setup Reimbursement
🏠 Telecomm Stipend
🏝 Flexible Time Off (FTO) + Holidays
🚀 Quarterly Team Gatherings
🥪 In office Perks*
Eve Legal is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation during the application process, reach out to your recruiter.
We may use artificial intelligence (AI) tools to support parts of the hiring process. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Location & Eligibility
Listing Details
- Posted
- September 15, 2026
- First seen
- September 15, 2026
- Last seen
- September 16, 2026
Posting Health
- Days active
- 0
- Repost count
- 0
- Trust Level
- 87%
- Scored at
- September 15, 2026
Signal breakdown
Eve Legal provides innovative AI-driven solutions that optimize the case management process for plaintiff law firms, enhancing efficiency and revenue potential.
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Senior Analytics EngineerUSD 190000-250000