Data Engineer

LiveKit

30d

About The Position

As a Data Engineer at LiveKit, you'll own the analytics infrastructure that powers our business intelligence and data analysis capabilities. Working closely with the Head of Data and analytics peers, you'll design and implement scalable GCP-based data pipelines — from ingestion through transformation to delivery — maximizing the GCP ecosystem for cost-effective solutions while integrating additional services or homegrown tooling where appropriate. While analytics infrastructure is the core focus, you'll also engage with the broader application data infrastructure, contributing your data pipeline expertise to support product and engineering needs. This is a foundational IC role with significant ownership over the architecture and direction of our analytics stack as the team grows.

Requirements

8+ years of experience in data engineering with strong Python and SQL expertise
Deep expertise in GCP, with hands-on experience in BigQuery, Dataflow, Cloud Storage, and related analytics services
Proven ability to design and implement production-grade data pipelines and aggregation layers for BI and analysis
AI-first development mindset with hands-on experience building AI-driven workflows and effectively using AI coding assistants
Strong understanding of data modeling, transformation patterns, and working with dbt
Experience with data movement tools (Estuary, Airbyte, Fivetran, or similar)
Solid infrastructure and DevOps fundamentals: Terraform or similar IaC, CI/CD, Git workflows, and change management
Experience implementing observability and monitoring for data systems (DataDog, Grafana, or similar)
Strong communication skills and ability to work cross-functionally with engineering and business stakeholders
Self-directed and comfortable with ambiguity in a fast-paced startup environment
Located in the US or Canada

Nice To Haves

Experience coordinating with dbt and analytics engineering teams
Background with AI workflow tools (n8n or similar)
Background with AI coding assistants
Prior experience as an early infrastructure hire building from the ground up

Responsibilities

Own the Analytics Infrastructure: You are the end-to-end owner of our GCP-based data infrastructure — including ingestion, movement, storage, security, and availability. You build and operate reliable, scalable pipelines that power analytics, and partner closely with the Analytics team on downstream transformation and BI.
Maximize the GCP Ecosystem: Build cost-effective solutions anchored in GCP-native services. Know when to extend with third-party tooling or homegrown solutions, and make pragmatic tradeoffs.
Contribute Across Data Infrastructure: While analytics is the primary focus, you'll bring broad data pipeline expertise to application data needs in collaboration with the product engineering team.
Managed Services First: Favor managed solutions over self-hosting. Evaluate build vs. buy with cost and operational burden in mind.
Engineering Standards: This role reports to the Head of Data within the Engineering org. Expect PR reviews, automated testing, proper change management, and production-grade standards.
AI-First Development: Work extensively with AI coding assistants and contribute to evolving our AI development workflows and infrastructure.
Startup Pace: Priorities shift quickly. Balance long-term architectural thinking with the tactical execution the moment requires.