• Design, build, and maintain the data storage and integration components of a mission-critical federal program. • Ensure enterprise databases are well-designed, high-performing, and reliable to support data integration objectives. • Design and implement database schemas and features, including complex SQL queries, stored procedures, and database code. • Monitor and optimize database performance through query tuning, indexing, and resource management. • Perform database optimization, performance tuning, and configure high-availability solutions to support large data volumes. • Oversee routine maintenance tasks such as backups, recovery procedures, and patching. • Implement and maintain robust disaster recovery strategies and backup policies. • Enforce database security controls, including access management, encryption, and hardening procedures. • Collaborate closely with data engineers and integration developers to ensure database structures support ETL/ELT processes. • Validate that database schemas align with incoming data feeds and ensure smooth operation of interfaces used for data integration.

GC AI is the fastest-growing and most trusted legal AI platform for in-house legal teams. We're building the future of legal work, and we're doing it fast. You'll join at a pivotal moment—when decisions matter, impact is immediate, and the runway to shape your career is wide open. We’re a high-performing team where you'll have real ownership and influence from day one. More than 1,800 companies use GC AI to drive their business forward, including 150+ public companies, 25+ unicorns, and brands such as News Corp, Miro, Bass Pro Shops, Snyk, Skims, Liquid Death, Vercel, Zscaler, and TIME. We've 10x'd revenue in 12 months, raised a $60 million Series B ($555 million valuation), and are growing faster than ever. We are backed by incredible investors, including Scale Venture Partners, Northzone, Sound Ventures, and Guillermo Rauch, CEO of Vercel. If you thrive when the stakes are high and the path isn't paved, you'll love it here. Our six guiding principles are: 1% better every day, customer obsession, ship today, find a way, care deeply, and own it completely. Come shape the future of legal work with us. 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 PaceWe’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 PolicyThis 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 EmploymentGC AI is an equal opportunity employer that supports workplace diversity and does not discriminate on the basis of race, color, religion, gender identity/expression, national origin, age, military service eligibility, veteran status, sexual orientation, marital status, physical or mental disability, or any other protected class. GC AI is committed to working with and providing reasonable accommodation to applicants with physical and mental disabilities. #LI-GCAI Fraud Notice to GC AI ApplicantsTo protect yourself against phishing and recruitment fraud, please note that GC AI only accepts job applications through our official careers page at https://gc.ai/careers and through sponsored jobs on LinkedIn. All legitimate communication from our team regarding job opportunities will come from a GC AI team member with a @gc.ai or @getgc.ai email address. GC AI will never: - Refer you to external websites to apply - Conduct interviews over email, chat platforms, or messaging apps - Ask you to provide payment or purchase equipment - Request personal or financial information such as your mailing address, social security number, credit card numbers, or banking information during the application process Examples of fraudulent email addresses: - info.gcai.careers.com@gmail.com - info.gc.aicareers.online.com@gmail.com - Any email address ending in @gmail.com, @yahoo.com, or other free email services If you are contacted by someone claiming to be from GC AI via an unofficial channel or from a suspicious email address, please do not share any information. Mark the communication as "phishing" or "spam" and do not respond.

• Design and build a modern, well-orchestrated data warehouse and pipeline architecture. • Build alerting, monitoring, and data-quality checks so the data platform can be trusted across the business. • Turn statistical and machine learning models into reliable, scalable production services. • Build the ML Ops stack and the tooling and context layers that let AI agents operate on our data reliably, including knowledge platforms on top of the data foundation. • Work closely with data scientists and analysts to translate their needs into architecture and tooling decisions.

• Build the Data Platform • Design, build, and maintain scalable ELT/ETL pipelines integrating product data, payment providers, and third-party APIs • Architect and own the company's cloud data warehouse (Snowflake, BigQuery, or Redshift) • Build reliable orchestration workflows using Airflow, Prefect, or similar tools • Optimize warehouse performance and cost through efficient data modeling and query optimization • Ensure Data Quality & Governance • Develop clean, testable transformation layers using dbt or equivalent frameworks • Design a semantic layer that provides consistent business metrics across the organization • Implement data quality testing, monitoring, lineage, and documentation • Build security and governance into the platform, including access controls, PII handling, and privacy-aware data practices • Partner Across Engineering & Product • Work closely with Product and Growth teams to support product analytics, experimentation, subscription metrics, and business reporting • Collaborate with software engineers on event tracking, data contracts, and API integrations • Promote DataOps best practices, including CI/CD, version control, testing, and documentation-as-code