Principal Analytics Engineer

Eve

9h•$220,000 - $250,000

About The Position

Eve is hiring a Principal Analytics Engineer to own its go-to-market data model and establish standards for the analytics engineering team. The role will create governed definitions for core GTM metrics—including pipeline, funnel conversion, customer acquisition cost, win rate, sales cycle length, ARR, and bookings—modeled in the warehouse and semantic layer using data from HubSpot, DealHub, Apollo, and Clay. The Principal Analytics Engineer will also own the company-wide metrics certification framework and catalog, define modeling and semantic-layer standards, and resolve disagreements over metric definitions. The role serves analysts and stakeholders across Sales, Marketing, Customer Success, and RevOps, and is part of the central team. It reports to the Head of Data Engineering, who reports directly to the CEO. Eve describes itself as an AI-native legal technology company serving plaintiff law firms. Its product supports work across the stages of a case, from intake through resolution. The company states that it is trusted by over 1,000 law firms, has raised over $160 million, and is growing revenue 2X quarter over quarter.

Requirements

At least 8 years of experience in analytics engineering, including time at staff or senior individual-contributor level setting technical direction followed by others.
Extensive proficiency in SQL, data modeling, and data transformation.
Deep dbt expertise, including advanced modeling patterns, macros, packages, and testing.
Experience building slowly changing dimension (SCD) tables from multiple sources.
Expert knowledge of 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 go-to-market systems such as HubSpot, Salesforce, or similar CRM and marketing automation platforms.
Experience resolving conflicting metric definitions across teams and bringing stakeholders to a shared definition.
Experience setting modeling standards followed by others and reviewing contributions from outside the 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 skills, a habit of mentoring, and comfort building where a playbook does not yet exist.

Nice To Haves

Experience in a regulated or high-sensitivity data environment, such as legal, healthcare, or financial services.
Experience enabling analysts outside the core team to contribute production models.
B2B SaaS experience, especially selling to small and mid-sized businesses or professional services firms.

Responsibilities

Partner with analysts and stakeholders to build warehouse and semantic-layer models for GTM decisions, including pipeline, funnel, campaign, and revenue, using HubSpot, DealHub, Apollo, and Clay data.
Design, deploy, and maintain semantic models for core GTM metrics, including pipeline coverage, funnel conversion, CAC, win rate, sales cycle time, ARR, and bookings, as a trusted foundation for analytics and AI consumption.
Build on the foundational source-to-staging models and conformed dimensions owned by data engineers.
Instrument models with the team's alerting so failures and drift surface proactively.
Maintain documentation for owned models, metrics, and definitions.
Stand up internal AI agents and data-grounded tools that provide GTM stakeholders with direct, trustworthy answers.
Own the metrics certification framework and catalog, defining governed metrics, their owners, and how stakeholders identify them.
Define modeling standards and semantic-layer patterns for analytics engineers and contributing analysts.
Partner with analysts building on the semantic layer and decide which definitions become certified and governed.
Arbitrate conflicting metric definitions across teams and establish a common definition.
Establish standards for analytical application development in Omni and Hex.
Work with stakeholders across Sales, Marketing, and Customer Success to turn questions into durable models rather than one-off answers.
Mentor analytics engineers and set the technical bar for analytics engineering at Eve.