GC AI - Founding Data Engineer, AI Platform
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Requirements
• Experience building internal data tools or agents using LLMs (text-to-SQL, natural language interfaces, automated reporting). This is a strong differentiator. • Experience as the first or early data hire at a startup, where you owned the full stack. • Familiarity with legaltech, legal operations, or SaaS product analytics. • Experience setting up self-serve analytics layers (semantic layers, BI tool configuration, data documentation). • Experience with data infrastructure that supports ML workflows (feature stores, training data pipelines, data lakes). • Experience with infrastructure as code, especially Terraform, for managing GCP data infrastructure. • 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. • Equal Opportunity Employment
Responsibilities
• Take ownership of the data warehouse in BigQuery: modeling, pipeline development, data quality, and performance. • Build pipelines that consolidate product usage data, CRM data, billing, customer contract data, and user analytics into a single source of truth. • Design and build internal data tools using applied AI, including natural-language query interfaces and automated reporting, so the rest of the company can self-serve without waiting on an analyst. • Set up the warehouse so business teams can run their own queries and pull their own numbers without filing a ticket. • Build toward a data lake architecture that supports personalization and model fine-tuning for the GC AI product. • Keep the stack lean. Use what's available in BigQuery and the broader GCP ecosystem and make smart decisions to reduce complexity and cost without introducing tool sprawl. • Define data engineering practices, tooling, and standards as the first hire on what will become a team. • Builder instinct. Your default response to a data problem is to write code and build infrastructure, not to evaluate vendors and sign contracts. • Applied AI fluency. You're comfortable using LLMs and AI tooling to solve data problems: building agents that query warehouses, automating data quality checks, generating reports. You don't need to train models, but you should know how to put them to work. • Simplicity bias. You'd rather build a clean, maintainable pipeline with standard tools than an over-engineered stack that requires a team of five to operate. • Ownership. You treat the data warehouse like a product: it should be reliable, well-documented, and useful to the people who depend on it. • What We Require • 5+ years of experience in data engineering, with hands-on experience building and maintaining data warehouses and pipelines. • Strong SQL skills and deep experience with BigQuery or comparable analytical databases. • Proficiency in Python for pipeline development, scripting, and tooling. • Experience building ETL/ELT pipelines that consolidate data from multiple source systems (SaaS APIs, event streams, databases). • Experience working within GCP or a comparable cloud ecosystem. • Ability to design data models that are clean, performant, and usable by non-engineers.
Benefits
• $165K – $350K • Offers Equity • Final compensation will vary based on leveling, market conditions, geographic location, and candidate qualifications, including relevant knowledge, skills, and experience assessed during the interview process. • Upload your resume here to autofill key application fields. • Drop your resume here! • Parsing your resume. Autofilling key fields... • or drag and drop here • What motivated you to apply to GC AI? • GC AI Privacy Policy • Another Gender Identity • I prefer not to answer • Asian or Asian American • Black or African American • Hispanic or Latine • Indigenous or Native American • Native Hawaiian or Other Pacific Islander • Recruiting Privacy Policy
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