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: 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.

What we offer

Innovative culture: You’ll be part of a generational opportunity to define the trajectory of AI, surrounded by a team pushing the boundaries of what’s possible.
Growth paths: Joining ElevenLabs means joining a dynamic team with countless opportunities to drive impact - beyond your immediate role and responsibilities.
Learning & development: ElevenLabs proactively supports professional development through an annual discretionary stipend.
Social travel: We also provide an annual discretionary stipend to meet up with colleagues each year, however you choose.
Annual company offsite: Each year, we bring the entire team together in a new location - past offsites have included Croatia and Italy.
Co-working: If you’re not located near one of our main hubs, we offer a monthly co-working stipend.

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.