About The Role

We are seeking an Analytics Engineer to join Revenue Operations and build the intelligence layer that turns GC AI's data into decisions. Reporting to Emma Heist, Head of Revenue Operations, you will be GC AI's first dedicated analytics hire, working closely with Sales, Marketing, Customer Success, Finance, Product, and leadership to define how the company measures itself and where it focuses.

You won't be building the warehouse from scratch (our Data Engineering team owns that), but you will own everything that sits on top of it: the data models that reflect how the business actually works, the dashboards and reporting that teams rely on daily, the KPI frameworks that drive GTM strategy, and the self-serve analytics layer that lets anyone answer their own questions. You will also partner closely with Data Engineering to ensure the warehouse schema and pipeline design support the analytical use cases the business needs.

We're looking for someone who combines strong technical chops in SQL, data modeling, and BI tooling with genuine curiosity about business operations and a knack for translating messy business questions into clean analytical frameworks.

What You'll Do

• Own the Analytical Modeling Layer: Design and maintain dimensional models (revenue, pipeline, product usage, retention, customer lifecycle) that make warehouse data usable for business teams. Partner with Data Engineering on schema design to ensure the warehouse serves analytical use cases.
• Create the Intelligence Layer: Build dashboards, reports, and self-serve analytics that give Sales, Marketing, Customer Success, Product, Finance, and leadership real-time visibility into the metrics that matter. Define KPIs, build attribution models, and create the single source of truth for company performance.
• Drive GTM Analytics: Own the analytical frameworks behind pipeline generation, sales forecasting, customer health scoring, retention analysis, and marketing attribution. Be the person who can tell the exec team not just what happened, but why, and what to do about it.
• Build Analytics Workflows: Build lightweight transformation and enrichment pipelines specific to analytics workflows (e.g., attribution logic, cohort tagging, KPI rollups) using dbt or similar tools. Implement data quality checks and governance practices to keep analytics accurate and trustworthy as we scale.
• Enable Data-Driven Culture: Partner with stakeholders across Revenue Operations, Finance, Product, and the exec team to understand their data needs, translate business questions into analytical frameworks, and make data accessible to non-technical users. You'll be the go-to person when someone asks, "Where does this number come from?"
• Scale the Function: Establish standards, documentation, and best practices to enable GC AI's analytics capabilities to grow. As the first analytics hire, you'll shape the roadmap for the analytics function's evolution and help recruit the next members.

What You've Done

• 5+ years in data engineering, business intelligence, or analytics engineering roles in B2B SaaS, with hands-on experience building data infrastructure from early-stage or greenfield environments.
• Experience building and maintaining analytical data models, semantic layers, or transformation layers using tools like dbt, Looker modeling, or similar frameworks.
• Strong proficiency in SQL, with experience modeling data in cloud warehouses such as BigQuery or Snowflake. Working knowledge of Python for scripting and automation.
• Track record building dashboards and reporting in BI tools (e.g., Looker, Tableau, or Sigma) that business teams rely on daily.
• Experience working with data from multiple SaaS systems (CRM, billing, product analytics, support platforms) and building unified, governed analytical models on top of them.
• Strong cross-functional collaboration with Sales, Marketing, Finance, Product, and leadership to align data work with business priorities and GTM goals.
• Analytical mindset with expertise in defining and tracking KPIs (e.g., ARR, pipeline velocity, conversion rates, retention, NRR) and using data to optimize go-to-market performance.
• Self-starter who thrives in fast-paced, ambiguous startup settings while balancing long-term architectural thinking with the need to ship today.

Nice to have

• Experience working alongside a data engineering team to influence warehouse design from the analytics consumer's perspective.
• Familiarity with product-led growth analytics, usage-based metrics, or AI/ML product instrumentation.
• Experience with data governance, data contracts, or data observability tooling (e.g., Monte Carlo, Great Expectations).

A Note On Pace

We're building something new in a once-in-a-generation shift in technology and the legal industry, so we move at a relentless pace. We expect urgency, ownership, and good judgment even when things aren't perfectly clear. If you need structure and consensus to do your best work, this isn't the right place for you. If you thrive in ambiguity and growth, work with intensity, and want real responsibility, keep reading. We're excited to meet you.

Location Policy

This is a remote role unless you fall within the following parameters. If you live within approximately 50 miles of our San Mateo, CA or Provo, UT office, the position follows a hybrid schedule with in-office days on Tuesdays, Wednesdays, and Thursdays.