Engineer Jobs

About The Position

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.

Benefits

• Competitive salary and equity.
• 401(k) program with employer matching.
• Health, dental, vision, and life insurance.
• Short-term and long-term disability coverage.
• Commuter benefits.
• Autonomous work environment.
• Workplace setup reimbursement.
• Telecomm stipend.
• Flexible time off (FTO) and holidays.
• Quarterly team gatherings.
• In-office perks for in-office employees.