Analytics Engineer

Role overview Own the data transformation and metrics layer that turns raw information into dependable decisions. As an early analytics engineering hire, you’ll establish shared modeling standards and certified measures across product, finance, and company-wide reporting, while helping make trusted data usable in AI-driven workflows.

Responsibilities - Build and maintain the dbt project, metrics layer, modeling conventions, tests, CI, and documentation. - Define certified product measures such as activation, adoption, retention, and usage. - Develop finance and company-wide KPI models, including billing, revenue, and annual recurring revenue measures. - Partner with data engineering on warehouse architecture and with stakeholders to shape reusable data products. - Support the delivery of reliable data to analysts, data scientists, and AI agents.

Requirements - Experience building and scaling a dbt project and metrics layer. - Expert dbt and SQL skills, with strong understanding of dimensional modeling, data grain, and incremental patterns. - Proficiency with a modern data stack, such as BigQuery or a similar warehouse, Python, and BI tools. - Strong data quality practices, including testing, documentation, reconciliation, and catching subtle modeling errors. - Ability to clarify stakeholder needs and turn them into general, well-documented models.

Nice to have Experience with billing, subscriptions, ARR, or high-volume product event data; data models for LLM agents or semantic layers; and data CI, contracts, privacy tags, or infrastructure-as-code for warehouse access.

Benefits and work setup Remote-first across Europe, with availability to work in GMT or GMT+1; optional offices in London and Warsaw. Support includes professional development and coworking stipends, an annual team gathering, and a stipend for meeting colleagues.