Responsibilities
- Own the transformation layer in dbt - design, build, and maintain modular, well-tested data models that define how data is structured and consumed across the company.
- Define and implement core business metrics (e.g. activation, engagement, retention) as reusable, versioned data assets - ensuring consistent definitions across analytics, product, and AI use cases.
- Model complex SaaS data by integrating product events, CRM (Salesforce), and support data into clean, well-defined fact and dimension models.
- Build and evolve our semantic layer - creating a reliable abstraction over our data that enables consistent KPI definitions and supports downstream consumers, including LLM-based analytics agents.
- Collaborate with Data Engineers on upstream data contracts and event schemas - ensuring raw data is structured in a way that supports scalable, reliable analytics.
- Establish and enforce best practices in testing, documentation, and data quality - making these part of the standard development lifecycle.
- Document models, metrics, and lineage clearly - enabling self-service and reducing ambiguity across teams.
Requirements
- 5+ years in analytics engineering or data engineering with a strong focus on data modeling
- Strong proficiency in dbt and SQL - building modular, well-tested models
- Solid understanding of dimensional modeling and metric design
- Experience working with cloud data warehouses (BigQuery, Snowflake, or Redshift)
- Experience with metrics / semantic layers (e.g. dbt metrics, MetricFlow, Cube)
- Strong data quality mindset (testing, validation, monitoring)
- Comfortable working with event-based data and cross-functional teams
- Able to turn ambiguous business questions into clear data models
- Strong business acumen with the ability to challenge metric definitions and ensure they reflect real business outcomes
- Fluent in English
Nice to Have
- Familiarity with how LLMs consume structured data - e.g. semantic layers, metrics registries, YAML-based context - and an interest in building data infrastructure that serves AI agents, not just BI tools.
- Experience modeling product usage data (event-based or session-based)
Benefits
- Work/Life balance: Flexible hours, 33 vacation days
- Wellbeing and financial support: Access to Open Up, corporate discounts
- Connection & community: Virtual events, collaborative team activities, and opportunities for local meet-ups
- And the list goes on: Tech equipment, referral bonuses, dog-friendly HQ
Work Arrangement
Hybrid
Additional Information
- Candidates must have work authorization in one of these countries.
- Office access available in London, Dublin, and Lisbon.
