Staff Analytics Engineer

The Role

We're hiring a Staff Analytics Engineer to own Eve's go-to-market data model and set the standard the rest of the analytics engineering team works inside.

The number of people asking the data a question is growing faster than the business, and the business is doubling. Today the same question can produce different answers depending on who asks and which tool they open. Your job is to make every core GTM metric resolve to one governed definition: pipeline generated, funnel conversion, CAC, win rate, sales cycle length, ARR and bookings, modeled in the warehouse and semantic layer from HubSpot, DealHub, Apollo, and Clay rather than inside those tools.

As the Staff engineer, GTM is your domain but not your limit. You'll own the certification framework and catalog that determine what counts as trusted across every domain, set the modeling standards other analytics engineers and analysts build inside, and make the calls when two teams define the same thing differently. Your users span analysts and stakeholders across Sales, Marketing, CS, and RevOps.

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.

What You'll Do

Model GTM

• Partner with analysts and stakeholders to build the models that power Eve's GTM decisions: pipeline, funnel, campaign, and revenue, sourced from HubSpot, DealHub, Apollo, and Clay and built in the warehouse and semantic layer rather than inside those tools
• Design semantic models for core GTM SaaS metrics (pipeline coverage, funnel conversion, CAC, win rate, sales cycle time, ARR and bookings), then own deploying and maintaining them as the trusted foundation for both analytics and AI consumption
• Build on the foundational layer the data engineers own: source-to-staging models and conformed dimensions.
• 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
• 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

Originally posted on Himalayas