Senior Analytics Engineer

Overview

In this role you will own the transformation layer with dbt, building modular data models and a reliable semantic layer to power analytics and AI agents. You will define and maintain core business metrics, model SaaS data across product, CRM, and support data, and collaborate with cross-functional teams to ensure data quality and self-service. You’ll shape how data is consumed across analytics, product, and AI use cases, enabling scalable, trusted insights. This is a mission-driven opportunity to advance data infrastructure that supports security awareness scale-ups in Europe.

Pay / Benefits

• flexible hours
• 33 vacation days
• wellbeing and financial support
• virtual events
• local meet-ups
• tech equipment

Responsibilities

• Design, build, and maintain modular dbt data models that structure and consume data across the company
• Define and implement core metrics (activation, engagement, retention) as reusable data assets
• Model complex SaaS data by integrating product events, Salesforce CRM, and support data into dimensional models
• Develop and evolve the semantic layer to support KPI definitions and downstream consumers, including AI analytics agents
• Collaborate with Data Engineers on upstream data contracts and event schemas to ensure scalable analytics
• Establish and enforce data quality, testing, and documentation as part of the development lifecycle
• Document models, metrics, and lineage to enable self-service and reduce ambiguity across teams

Key requirements

• 5+ years in analytics engineering or data engineering with a focus on data modeling
• Strong proficiency in dbt and SQL
• Solid understanding of dimensional modeling and metric design
• Experience 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 with event-based data and cross-functional collaboration
• Ability to turn ambiguous business questions into clear data models
• Strong business acumen and ability to challenge metric definitions
• Fluent in English

• cross-functional collaboration
• problem-solving
• communication
• dbt
• SQL
• dimensional modeling

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