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ElevenLabs is an AI research and product company that has raised $781M in funding with a $22B valuation. The company serves millions of users and thousands of businesses including Deutsche Telekom and Meta, with investors including Andreessen Horowitz, ICONIQ Growth, and Sequoia.
You will join the Data team as one of its first hires, owning the transformation layer between raw data and business decisions. This is a foundational role where you will establish data standards and practices that scale across the organization.
Key responsibilities:
- Drive implementation of a world-class metric layer, including modelling conventions, project structure, testing, CI, and documentation
- Design and maintain certified KPI layers 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
- Work with stakeholders to translate ambiguous questions into trusted, reusable data products, including data served through AI agents
The role is remote-first and can be executed from anywhere in Europe or the US East Coast, with the ability to operate in CET/ET timezones required. Optional office locations include London, Warsaw, Dublin, or New York.
ElevenLabs operates with a high-velocity, impact-focused culture emphasizing rapid experimentation, lean autonomous teams, and minimal bureaucracy. The company uses AI across the organization and prioritizes talent over location.
REQUIREMENTS:
- 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: testing, documentation, and reconciliation by default; ability to spot models that run cleanly but are 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
- Serving data to LLM-based agents or building semantic/metrics layers consumed by AI tools
- CI for data (Slim CI, data contracts, PII policy tags) and infrastructure-as-code for warehouse access
