Analytics Engineer

Description

About the company

The company is an AI research and product company transforming how we interact with technology.

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.

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.

ElevenCreative empowers creators and marketers to generate and edit speech, music, image, and video across 70+ languages.

ElevenAPI gives developers access to our leading AI audio foundational models.

Everything we do is the result of the creativity and commitment of our team - builders doing the best work of their lives. We are researchers, engineers, and operators. IOI medalists and ex-founders. If you want to work hard and create lasting positive impact, we want to hear from you.

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.

Conditions

High-velocity: Rapid experimentation, lean autonomous teams, and minimal bureaucracy.

Impact not job titles: We don’t have job titles. Instead, it’s about the impact you have. No task is above or beneath you.

AI first: We use AI to move faster with higher-quality results. We do this across the whole company—from engineering to growth to operations.

Excellence everywhere: Everything we do should match the quality of our AI models.

Global team: We prioritize your talent, not your location.

This role is remote-first, so it can be executed from anywhere in Europe or the US East Coast, with the ability to operate in CET / ET timezones required. If you prefer, you can work from our offices in London, Warsaw, Dublin or New York.

LI-Remote

We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or other legally protected statuses.

Benefits

• 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 the company means joining a dynamic team with countless opportunities to drive impact - beyond your immediate role and responsibilities.
• Learning & development : the company 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.
• About the role
• 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 the company 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.