Analytics Engineer

We launched in January 2023 with the first human-like AI voice model. Today, we serve millions of users and thousands of businesses - from fast-growing startups to large enterprises like Deutsche Telekom and Meta. Our investors are some of the world's most prominent, including Andreessen Horowitz, ICONIQ Growth and ...

We have expanded from voice into three main platforms:

ElevenAgents enables businesses to deliver seamless and intelligent customer experiences, with the integrations, testing, monitoring, and reliability necessary to deploy voice and chat agents at scale.

We're looking for an experienced Analytics Engineer to join our Data team as one of its first hires. You will own the transformation layer between raw data and business decisions: modelling our data in dbt, defining the certified metrics the company runs on, and setting the standards that every analyst and data scientist at ElevenLabs builds.
Drive the implementation of a world-class metric layer for the company: modelling conventions, project structure, testing, CI, and documentation.
Design and maintain the certified KPI layer for product (activation, adoption, retention, usage), finance (billing, ARR, revenue), and company-wide topics, built to withstand audit scrutiny.
Partner with Data Engineering on evolving the data warehouse architecture, and with stakeholders to turn ambiguous questions into trusted, reusable data products, including data served through AI agents.

Demonstrated experience building and scaling a dbt project and metrics layer, ideally as one of the first analytics engineers on a growing team.
Expert knowledge of dbt and advanced SQL (required); strong grasp of dimensional modelling, grain, and incremental patterns.
Proficiency across the modern data stack (BigQuery or similar warehouse, Python, BI tools such as Looker).
Rigour around data quality: you test, document, and reconcile by default, and you spot a model that runs cleanly but is quietly wrong.
Strong ability to understand varied stakeholder requirements, push back where needed, and translate them into generalised, well-documented models.
Bonus:
Experience modelling billing, subscription and ARR data (Stripe, Salesforce) or product event data at scale.
Experience serving data to LLM-based agents or building semantic / metrics layers consumed by AI tools.
Experience with CI for data (Slim CI, data contracts, PII policy tags) and infrastructure-as-code for warehouse access.