Staff Analytics Engineer

Eve

23h•$226,000 - $265,000

About The Position

Eve is hiring a Staff Analytics Engineer to own its go-to-market data model and establish standards for the analytics engineering team. The role will make core GTM metrics resolve to consistent, governed definitions, modeled in the warehouse and semantic layer from systems such as HubSpot, DealHub, Apollo, and Clay. Metrics include pipeline generated, funnel conversion, customer acquisition cost, win rate, sales cycle length, ARR, and bookings. GTM is the role’s primary domain, but the position also owns the company-wide metrics certification framework and catalog, establishes modeling standards, and adjudicates conflicting metric definitions across teams. The role serves analysts and stakeholders across Sales, Marketing, Customer Success, and Revenue Operations, and includes building internal AI agents and data-grounded tools for trusted self-service answers. The Staff Analytics Engineer will work on Eve’s central team and report to the Head of Data Engineering, who reports directly to the CEO. The role partners with data engineers, analysts, and business stakeholders to build durable models, maintain a trusted semantic layer, and mentor analytics engineers.

Requirements

At least 8 years of experience in analytics engineering, including experience at staff or senior individual-contributor level setting technical direction others followed.
Extensive proficiency in SQL, data modeling, and data transformation, with deep dbt expertise including advanced modeling patterns, macros, packages, and testing.
Experience building slowly changing dimension (SCD) tables from multiple sources.
Expert knowledge of the modern data stack, including 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 inconsistent metric definitions across teams and aligning stakeholders on a single definition.
Experience setting modeling standards that others follow and reviewing contributions from outside the immediate 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 in an environment without an established playbook.

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 midsized businesses or professional services firms.

Responsibilities

Partner with analysts and stakeholders to build warehouse and semantic-layer models for GTM pipeline, funnel, campaign, and revenue data from HubSpot, DealHub, Apollo, and Clay.
Design, deploy, and maintain semantic models for core GTM metrics, including pipeline coverage, funnel conversion, CAC, win rate, sales cycle time, ARR, and bookings.
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 data drift are identified 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 what qualifies as governed, who owns each definition, and how stakeholders can identify certified metrics.
Define modeling standards and semantic-layer patterns for analytics engineers and contributing analysts.
Partner with analysts building on the semantic layer and make certification decisions about which definitions become governed.
Arbitrate conflicting metric definitions across teams and establish a consistent definition.
Establish standards and patterns for analytical application development in Omni and Hex.
Work with Sales, Marketing, and Customer Success stakeholders to turn questions into durable models rather than one-off answers.
Mentor analytics engineers and set the technical bar for analytics engineering at Eve.