Senior Analytics Engineer

We're hiring a Senior Analytics Engineer to own two domains: product analytics and customer success.

As Eve ships faster and the customer base grows, more people are asking whether a feature is landing, whether an account is healthy, and why a renewal slipped. Today the answer depends on who asks and which tool they open. Your job is to make every product and customer metric resolve to one governed definition: weekly active usage, adoption depth by feature, launch performance, customer health, onboarding time-to-value, net revenue retention, churn, and expansion.

Product and Customer Success are your stakeholders, and both are full domains. PMs tracking adoption and launch health. CS and RevOps working health scores, renewal risk, and expansion. The interesting work sits where they meet, since usage behavior is the best early signal of whether an account renews, and nobody can see that today.

You'll work on our central team and report to the Head of Data Engineering, who reports directly to the CEO. Data is a first-class function at Eve and the fuel to drive our future growth.

Model product and customer success

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

Partner and build

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

What We're Looking For

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 haves:

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