GC AI - Member of Technical Staff, Data Engineering
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. • 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. • How We Work Together • GC AI is a distributed company with team members across North America, and soon Europe. We believe that because our business is AI, human connection matters more, not less. We invest in bringing people together through our hub model: regular coworking sessions, customer dinners, team and company offsites. If you’re in a location without an office, expect up to 10% travel (and more for customer-facing roles). • Team members in the San Francisco Bay Area and Provo, UT (within 50 miles of the office) work together on Tuesdays, Wednesdays, and Thursdays. As we grow, we plan to establish permanent offices in more hub cities, and we will look to our team members to help build that in-person culture. We currently hire across the United States and Canada. • 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... • Please Note: we have set up limits for applications for this role. It is in the Engineering group. The following limits apply to applications for all jobs within this group: • Engineering • Candidates may not apply more than 3 times in any 90 day span for any job in the Engineering Group. • Engineering • Candidates may not re-apply to the same role within 365 days if not presented with an offer • 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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