Staff Analytics Engineer

Maintain documentation of the models, metrics, and definitions you own

Stand up internal AI agents and data-grounded tools that give GTM stakeholders a direct, trustworthy answer without waiting on a ticket

Set the standard

Own the metrics certification framework and catalog: what qualifies as a governed definition, who owns it, and how a stakeholder can tell at a glance

Define the modeling standards and semantic layer patterns every analytics engineer and contributing analyst works inside

Partner with analysts building on the semantic layer, and own the certification call on what becomes a governed definition

Arbitrate when two teams define the same metric differently, and make the call stick

Establish patterns and standards for analytical application development in Omni and Hex

Sit with stakeholders across Sales, Marketing, and Customer Success to turn open questions into durable models rather than one-off answers

Mentor analytics engineers and set the technical bar for how analytics engineering gets done at Eve

What We're Looking For

8+ years in analytics engineering, including time at a staff or senior IC level setting technical direction others followed

Extensive proficiency in SQL, data modeling, and transformation, with deep dbt expertise: advanced modeling patterns, macros, packages, and testing. Experience building SCD tables from multiple sources

Expert knowledge of the modern stack: Snowflake, dbt, and a semantic or BI layer such as Omni or Hex

Experience designing semantic models or metric layers for human and AI consumption

Experience modeling GTM systems (HubSpot, Salesforce, or similar CRM and marketing automation platforms)

You've resolved a "these numbers don't match" problem across teams that each believed their version, and can describe how you got everyone to one definition

Experience setting modeling standards others follow, and reviewing contributions from people outside your team

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, a habit of mentoring, 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 enabling analysts outside the core team to contribute production models

B2B SaaS, especially selling to small and mid-sized businesses or professional services firms