Senior Analytics Engineer

eve; United States; Remote; USD 100,000 - 140,000; Full time

Benefits offered by this job

Competitive salary

Equity

Health insurance

401(k) with matching

Flexible time off

Commuter benefits

Autonomous work environment

Job summary

Join eve as a Senior Analytics Engineer, taking ownership of product analytics and customer success metrics. You will work to unify metrics and drive insights that connect product usage to customer health and retention.

We are seeking someone with a strong analytics engineering background, particularly in SQL and dbt, and experience working collaboratively across teams. The role offers a competitive salary, equity, and a flexible work environment.

Qualifications

• 5+ years of analytics engineering experience owning projects end to end.
• Strong SQL, data modeling, and transformation skills.
• Deep dbt expertise including advanced patterns, macros, and testing.

Responsibilities

• Build and maintain data models tracking product usage and behavior-based metrics.
• Develop models powering customer success workflows.
• Govern definitions for NRR, churn, and expansion.
• Design semantic layers connecting usage behavior to customer outcomes.
• Partner with product teams on event taxonomy.
• Build internal AI agents and data-grounded tools.
• Document owned models and metrics, instrumenting them with alerting.
• Mentor and review analysts contributing models.

Skills

Analytics engineering

SQL

Data modeling

Transformation skills

dbt expertise

SCD tables

Snowflake

BI tools (Omni, Hex)

Amplitude

Mixpanel

Pendo

Communication skills

AI-assisted development

Job description

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