Data Engineer
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Job Description
Data Engineer at LiveKit managing analytics infrastructure for voice AI applications. Building scalable GCP-based data pipelines and engaging across data infrastructure.
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.
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.
Benefits:
- Competitive salary and equity package
- Health, dental, and vision benefits
- Flexible vacation policy
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