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ElevenLabs

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Be one of the first data engineers formalizing pipelines at a fast-scaling AI voice company

Interview for a remote Data Engineer role on ElevenLabs' Growth team, building and scaling data pipelines from ingestion to delivery. This role is about dbt mastery, self-service tooling, and being an early data hire who brings structure to messy, high-growth data — expect probing on dbt architecture decisions, stakeholder-driven data modeling, and how you'd build AI agents to serve data questions for non-technical teams.

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What this interview tests

  • Expert-level dbt: models, tests, best practices, and data standards
  • Building and owning data pipelines end-to-end (ingestion to delivery)
  • Self-service tooling and documentation for cross-functional stakeholders
  • Modern data stack fluency: Python, SQL, BI tools
  • Generalizing varied stakeholder requirements into reusable data models
  • Using AI/LLM agents to serve data questions

Common question themes

Walk me through a data pipeline you built or overhauled from ingestion to delivery

How have you implemented dbt best practices and data quality controls on a growing team

Describe a time you built self-service tooling that reduced ad hoc requests

How do you turn messy, varied stakeholder requirements into a clean, generalized data model

How would you approach building an AI agent to answer data questions for non-technical teams

Tell me about operating as one of the first data engineers on a team — what did you prioritize first

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