Analytics Engineer

LiveKit

About The Position

As our first data hire, you'll establish the foundation of LiveKit's analytics practice. You'll translate business concepts into robust data models, create actionable KPIs, and enable data-driven decision making across a fast-growing organization. This role requires balancing pragmatic short-term solutions with building for scale, working in an AI-first development environment where tooling like Claude Code and skills-based workflows are core to how we operate.

Requirements

8+ years experience in analytics or analytics engineering
Deep expertise with dbt: data modeling, testing, documentation, project architecture, and establishing best practices
Strong experience implementing and scaling BI platforms (Lightdash, Sigma, Omni, Hex, or similar)
Proven ability to work directly with business stakeholders to define metrics and requirements
Experience building analytics practices in startup or high-growth environments
Strong SQL and Python skills
Comfortable with software engineering practices: Git workflows, CI/CD, code review, change management
Self-directed and adaptable—able to switch between building for the long-term and pragmatic short-term solutions
Strong communication skills for both technical and non-technical audiences
Located in US or Canada

Nice To Haves

Experience with Lightdash or BI-as-code platforms
Background with AI coding agents (Claude Code, GitHub Copilot, Cursor)
Prior experience as an early data hire (first 1-3 on the team)
Understanding of modern data stack and lakehouse concepts

Responsibilities

AI-First Development: You'll be expected to use AI coding assistants extensively and evolve our AI development workflows. This includes creating reusable skills, establishing quality checks, and documenting processes that work for both humans and AI agents.
Start Simple, Stay Focused: Resist the urge to over-engineer. Get core business metrics right and adopted before expanding. We value working models over perfect architectures.
Business Partnership: You'll spend significant time with stakeholders memorializing business logic and creating trusted metrics. Building relationships and business expertise is as important as technical execution.
Documentation-Driven: Everything you build needs clear documentation that serves both human users and AI systems.
Engineering Standards: This role reports into Engineering and follows eng best practices. Expect PR reviews, automated testing, and production-grade standards.
Startup Pace: Priorities shift quickly. What you're building tomorrow might differ from today. Comfort with ambiguity and rapid iteration is essential.